1 2020 Full States EV and EC of One Check
This is the example vignette for function: snw_evuvw20_jaeemk from the PrjOptiSNW Package. 2020 integrated over VU and VW. Average C or V given unemployment probabilities.
1.1 Test SNW_EVUVW20_JAEEMK Defaults
Call the function with defaults.
clear all;
st_solu_type = 'bisec_vec';
% Solve the VFI Problem and get Value Function
mp_params = snw_mp_param('default_docdense');
mp_params('beta') = 0.95;
mp_controls = snw_mp_control('default_test');
% set Unemployment Related Variables
xi=0.5; % Proportional reduction in income due to unemployment (xi=0 refers to 0 labor income; xi=1 refers to no drop in labor income)
b=0; % Unemployment insurance replacement rate (b=0 refers to no UI benefits; b=1 refers to 100 percent labor income replacement)
TR=100/58056; % Value of a welfare check (can receive multiple checks). TO DO: Update with alternative values
mp_params('xi') = xi;
mp_params('b') = b;
mp_params('TR') = TR;
% Solve for Unemployment Values
mp_controls('bl_print_vfi') = false;
mp_controls('bl_print_ds') = false;
mp_controls('bl_print_ds_verbose') = false;
mp_controls('bl_print_precompute') = false;
mp_controls('bl_print_precompute_verbose') = false;
mp_controls('bl_print_a4chk') = false;
mp_controls('bl_print_a4chk_verbose') = false;
mp_controls('bl_print_evuvw20_jaeemk') = false;
mp_controls('bl_print_evuvw20_jaeemk_verbose') = false;Solve the model:
%% A. Solve VFI
% 2. Solve VFI and Distributon
% Solve the Model to get V working and unemployed
% solved with calibrated regular a2
[V_ss,ap_ss,cons_ss,mp_valpol_more_ss] = snw_vfi_main_bisec_vec(mp_params, mp_controls);
Completed SNW_VFI_MAIN_BISEC_VEC;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=523.3967
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ ______
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -1.2728e+08 -2.9126 20.655 -7.0915 -485.24 16.447
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3962e+09 31.95 36.423 1.14 0 160.21
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3374e+08 5.3487 8.4439 1.5787 0.036717 141.66
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -274.81 -274.42 -271.94 -266.29 -257.26 14.439 14.533 14.626 14.718 14.809
r2 -265.29 -264.9 -262.43 -256.84 -248.12 14.494 14.585 14.674 14.763 14.85
r3 -255.77 -255.38 -252.93 -247.53 -239.24 14.55 14.636 14.723 14.808 14.892
r4 -246.16 -245.8 -243.52 -238.46 -230.68 14.606 14.689 14.772 14.853 14.935
r5 -237.48 -237.14 -235.01 -230.26 -222.92 14.654 14.734 14.813 14.891 14.97
r79 -9.6662 -9.655 -9.5783 -9.3823 -9.0457 2.4698 2.4801 2.4898 2.4989 2.5075
r80 -8.7031 -8.6919 -8.6152 -8.4192 -8.0826 2.253 2.261 2.2685 2.2755 2.2822
r81 -7.5138 -7.5026 -7.4258 -7.2298 -6.8933 1.9749 1.9803 1.9855 1.9904 1.995
r82 -5.9155 -5.9043 -5.8275 -5.6315 -5.295 1.582 1.5851 1.588 1.5907 1.5933
r83 -3.5892 -3.578 -3.5012 -3.3052 -2.9687 0.97904 0.98004 0.98097 0.98185 0.98267
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __________ _________ ________ _______ _______ _______ _______ _______
r1 0 0 0.00051498 0.0066578 0.021589 112.13 117.66 123.39 129.3 135.72
r2 0 0 0.00051498 0.0057684 0.020245 112.16 117.7 123.42 129.34 135.75
r3 0 0 0.00020768 0.0041456 0.018539 112.19 117.72 123.45 129.36 135.77
r4 0 0 0.00010346 0.0041199 0.018307 112.85 118.38 124.11 130.02 136.44
r5 0 0 5.2907e-06 0.0041199 0.018091 113.53 119.06 124.78 130.7 137.11
r79 0 0 0 0 0 81.091 85.373 89.342 93.265 97.358
r80 0 0 0 0 0 76.137 79.759 83.442 86.995 90.589
r81 0 0 0 0 0 67.958 70.652 73.689 77.006 81.091
r82 0 0 0 0 0 50.126 53.467 56.319 57.902 60.587
r83 0 0 0 0 0 0 0 0 0 0
xxx TABLE:cons_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036717 0.037251 0.040477 0.044486 0.049324 12.272 12.557 12.851 13.152 13.152
r2 0.036717 0.037251 0.040477 0.045375 0.050668 12.508 12.794 13.089 13.391 13.391
r3 0.036717 0.037251 0.040784 0.046998 0.052374 12.762 13.05 13.345 13.646 13.646
r4 0.038144 0.038678 0.042314 0.048449 0.054031 13.008 13.297 13.593 13.891 13.891
r5 0.039534 0.040068 0.043802 0.049839 0.055635 13.245 13.534 13.83 14.125 14.125
r79 0.2179 0.21844 0.22216 0.23228 0.25197 35.858 37.4 39.448 41.74 44.062
r80 0.2179 0.21844 0.22216 0.23228 0.25197 40.785 42.986 45.321 47.983 50.804
r81 0.2179 0.21844 0.22216 0.23228 0.25197 48.942 52.071 55.052 57.95 60.279
r82 0.2179 0.21844 0.22216 0.23228 0.25197 66.755 69.238 72.404 77.036 80.765
r83 0.2179 0.21844 0.22216 0.23228 0.25197 116.87 122.69 128.71 134.92 141.34
% COVID year tax
mp_params('a2_covidyr') = mp_params('a2_covidyr_manna_heaven');
% 2020 V and C same as V_SS and cons_ss if tax the same
if (mp_params('a2_covidyr') == mp_params('a2'))
% mana from heaven
V_ss_2020 = V_ss;
cons_ss_2020 = cons_ss;
else
% change xi and b to for people without unemployment shock
% solving for employed but 2020 tax results
% a2_covidyr > a2, we increased tax in 2020 to pay for covid and other
% costs resolve for both employed and unemployed
xi = mp_params('xi');
b = mp_params('b');
mp_params('xi') = 1;
mp_params('b') = 0;
[V_ss_2020,~,cons_ss_2020,~] = snw_vfi_main_bisec_vec(mp_params, mp_controls, V_ss);
mp_params('xi') = xi;
mp_params('b') = b;
end
% Solve unemployment, with three input parameters, auto will use a2_covidyr
% as tax, similar for employed call above
[V_unemp_2020,~,cons_unemp_2020] = snw_vfi_main_bisec_vec(mp_params, mp_controls, V_ss);
Completed SNW_VFI_MAIN_BISEC_VEC 1 Period Unemp Shock;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=315.9153
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ ______
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -1.4885e+08 -3.4063 21.649 -6.3556 -544.41 16.415
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.36e+09 31.122 36.291 1.1661 0 143.39
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.2982e+08 5.2591 8.4465 1.6061 0.018623 140.5
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -301.27 -299.77 -291.24 -277.82 -265.42 14.357 14.455 14.551 14.646 14.74
r2 -291.76 -290.26 -281.72 -268.3 -256.02 14.413 14.507 14.6 14.692 14.782
r3 -282.23 -280.74 -272.2 -258.78 -246.76 14.469 14.56 14.649 14.737 14.824
r4 -271.61 -270.22 -262.26 -249.53 -238.04 14.522 14.609 14.695 14.78 14.864
r5 -262.02 -260.72 -253.26 -241.16 -230.13 14.567 14.65 14.733 14.815 14.896
r79 -9.6662 -9.655 -9.5783 -9.3823 -9.0457 2.4678 2.4783 2.4882 2.4974 2.5062
r80 -8.7031 -8.6919 -8.6152 -8.4192 -8.0826 2.2515 2.2596 2.2673 2.2745 2.2812
r81 -7.5138 -7.5026 -7.4258 -7.2298 -6.8933 1.9738 1.9794 1.9847 1.9896 1.9943
r82 -5.9155 -5.9043 -5.8275 -5.6315 -5.295 1.5815 1.5846 1.5875 1.5903 1.5929
r83 -3.5892 -3.578 -3.5012 -3.3052 -2.9687 0.97886 0.97987 0.98082 0.98171 0.98255
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __ __ _________ _______ _______ _______ _______ _______
r1 0 0 0 0 0.0083625 107.54 113.08 118.81 124.74 130.85
r2 0 0 0 0 0.0074731 107.44 112.98 118.71 124.63 130.75
r3 0 0 0 0 0.0058503 107.32 112.87 118.6 124.52 130.63
r4 0 0 0 0 0.0049981 107.53 113.08 118.81 124.72 130.84
r5 0 0 0 0 0.004174 107.75 113.3 119.02 124.94 131.06
r79 0 0 0 0 0 80.458 84.335 88.305 92.228 96.321
r80 0 0 0 0 0 75.113 78.735 82.418 85.971 90.439
r81 0 0 0 0 0 66.945 69.639 72.676 76.669 81.091
r82 0 0 0 0 0 50.126 53.467 55.315 56.953 60.587
r83 0 0 0 0 0 0 0 0 0 0
xxx TABLE:cons_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.018623 0.019158 0.022901 0.033062 0.044486 11.996 12.272 12.557 12.851 13.152
r2 0.018623 0.019158 0.022901 0.033062 0.045375 12.23 12.508 12.794 13.089 13.391
r3 0.018623 0.019158 0.022901 0.033062 0.046998 12.483 12.762 13.05 13.345 13.646
r4 0.019354 0.019888 0.023632 0.033792 0.048579 12.728 13.008 13.297 13.593 13.891
r5 0.020066 0.020601 0.024344 0.034504 0.050114 12.963 13.245 13.534 13.83 14.125
r79 0.2179 0.21844 0.22216 0.23228 0.25197 35.453 37.4 39.448 41.74 44.062
r80 0.2179 0.21844 0.22216 0.23228 0.25197 40.785 42.986 45.321 47.983 49.93
r81 0.2179 0.21844 0.22216 0.23228 0.25197 48.942 52.071 55.052 57.274 59.267
r82 0.2179 0.21844 0.22216 0.23228 0.25197 65.751 68.234 72.404 76.981 79.762
r83 0.2179 0.21844 0.22216 0.23228 0.25197 115.87 121.69 127.71 133.93 140.34
%% B. Solve Dist
[Phi_true] = snw_ds_main_vec(mp_params, mp_controls, ap_ss, cons_ss);
Completed SNW_DS_MAIN_VEC;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=1447.0669Previous code
% % Solve the Model to get V working and unemployed
% [V_ss,ap_ss,cons_ss,mp_valpol_more_ss] = snw_vfi_main_bisec_vec(mp_params, mp_controls);
% % Solve unemployment
% [V_unemp,~,cons_unemp,~] = snw_vfi_main_bisec_vec(mp_params, mp_controls, V_ss);
% [Phi_true] = snw_ds_main(mp_params, mp_controls, ap_ss, cons_ss, mp_valpol_more_ss);1.2 Precompute
inc_VFI = mp_valpol_more_ss('inc_VFI');
spouse_inc_VFI = mp_valpol_more_ss('spouse_inc_VFI');
total_inc_VFI = inc_VFI + spouse_inc_VFI;
% Get Matrixes
cl_st_precompute_list = {'a', ...
'inc', 'inc_unemp', 'spouse_inc', 'spouse_inc_unemp', 'ref_earn_wageind_grid'};
mp_controls('bl_print_precompute_verbose') = false;
[mp_precompute_res] = snw_hh_precompute(mp_params, mp_controls, cl_st_precompute_list, ap_ss, Phi_true);
Wage quintile cutoffs=0.4645 0.71528 1.0335 1.5632
Completed SNW_HH_PRECOMPUTE;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=271.57621.3 Solve for 2020 Evuvw With 0 and 2 Checks
% Call Function
welf_checks = 0;
[ev20_jaeemk_check0, ec20_jaeemk_check0] = snw_evuvw20_jaeemk(...
welf_checks, st_solu_type, mp_params, mp_controls, ...
V_ss_2020, cons_ss_2020, V_unemp_2020, cons_unemp_2020, mp_precompute_res);
Completed SNW_A4CHK_WRK_BISEC_VEC;welf_checks=0;TR=0.0017225;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=69.6695
Completed SNW_A4CHK_UNEMP_BISEC_VEC;welf_checks=0;TR=0.0017225;xi=0.5;b=0;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=69.776
Completed SNW_EVUVW20_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=8.4846
% Call Function
welf_checks = 2;
[ev20_jaeemk_check2, ec20_jaeemk_check2] = snw_evuvw20_jaeemk(...
welf_checks, st_solu_type, mp_params, mp_controls, ...
V_ss_2020, cons_ss_2020, V_unemp_2020, cons_unemp_2020, mp_precompute_res);
Completed SNW_A4CHK_WRK_BISEC_VEC;welf_checks=2;TR=0.0017225;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=71.1503
Completed SNW_A4CHK_UNEMP_BISEC_VEC;welf_checks=2;TR=0.0017225;xi=0.5;b=0;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=70.0588
Completed SNW_EVUVW20_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=8.0571Differences between Checks in Expected Value and Expected Consumption
mn_V_U_gain_check = ev20_jaeemk_check2 - ev20_jaeemk_check0;
mn_MPC_U_gain_share_check = (ec20_jaeemk_check2 - ec20_jaeemk_check0)./(welf_checks*mp_params('TR'));1.4 Param Results Define Frames
Define the matrix dimensions names and dimension vector values. Policy and Value Functions share the same ND dimensional structure.
% Grids:
age_grid = 18:100;
agrid = mp_params('agrid')';
eta_H_grid = mp_params('eta_H_grid')';
eta_S_grid = mp_params('eta_S_grid')';
ar_st_eta_HS_grid = string(cellstr([num2str(eta_H_grid', 'hz=%3.2f;'), num2str(eta_S_grid', 'wz=%3.2f')]));
edu_grid = [0,1];
marry_grid = [0,1];
kids_grid = (1:1:mp_params('n_kidsgrid'))';
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
cl_mp_datasetdesc = {};
cl_mp_datasetdesc{1} = containers.Map({'name', 'labval'}, {'age', age_grid});
cl_mp_datasetdesc{2} = containers.Map({'name', 'labval'}, {'savings', agrid});
cl_mp_datasetdesc{3} = containers.Map({'name', 'labval'}, {'eta', 1:length(eta_H_grid)});
cl_mp_datasetdesc{4} = containers.Map({'name', 'labval'}, {'edu', edu_grid});
cl_mp_datasetdesc{5} = containers.Map({'name', 'labval'}, {'marry', marry_grid});
cl_mp_datasetdesc{6} = containers.Map({'name', 'labval'}, {'kids', kids_grid});1.5 Analyze Difference in V and C with Check
The difference between V and V with Check, marginal utility gain given the check.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
mp_support_graph('cl_st_xtitle') = {'Savings States, a'};
mp_support_graph('st_legend_loc') = 'eastoutside';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('it_legend_select') = 21; % how many shock legends to show
mp_support_graph('cl_colors') = 'jet';MEAN(MN_V_GAIN_CHECK(A,Z))
Tabulate value and policies along savings and shocks:
% Set
ar_permute = [1,4,5,6,3,2];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(A,Z)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(A,Z)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15 mean_eta_16 mean_eta_17 mean_eta_18 mean_eta_19 mean_eta_20 mean_eta_21 mean_eta_22 mean_eta_23 mean_eta_24 mean_eta_25 mean_eta_26 mean_eta_27 mean_eta_28 mean_eta_29 mean_eta_30 mean_eta_31 mean_eta_32 mean_eta_33 mean_eta_34 mean_eta_35 mean_eta_36 mean_eta_37 mean_eta_38 mean_eta_39 mean_eta_40 mean_eta_41 mean_eta_42 mean_eta_43 mean_eta_44 mean_eta_45 mean_eta_46 mean_eta_47 mean_eta_48 mean_eta_49 mean_eta_50 mean_eta_51 mean_eta_52 mean_eta_53 mean_eta_54 mean_eta_55 mean_eta_56 mean_eta_57 mean_eta_58 mean_eta_59 mean_eta_60 mean_eta_61 mean_eta_62 mean_eta_63 mean_eta_64 mean_eta_65 mean_eta_66 mean_eta_67 mean_eta_68 mean_eta_69 mean_eta_70 mean_eta_71 mean_eta_72 mean_eta_73 mean_eta_74 mean_eta_75 mean_eta_76 mean_eta_77 mean_eta_78 mean_eta_79 mean_eta_80 mean_eta_81 mean_eta_82 mean_eta_83 mean_eta_84 mean_eta_85 mean_eta_86 mean_eta_87 mean_eta_88 mean_eta_89 mean_eta_90 mean_eta_91 mean_eta_92 mean_eta_93 mean_eta_94 mean_eta_95 mean_eta_96 mean_eta_97 mean_eta_98 mean_eta_99 mean_eta_100 mean_eta_101 mean_eta_102 mean_eta_103 mean_eta_104 mean_eta_105 mean_eta_106 mean_eta_107 mean_eta_108 mean_eta_109 mean_eta_110 mean_eta_111 mean_eta_112 mean_eta_113 mean_eta_114 mean_eta_115 mean_eta_116 mean_eta_117 mean_eta_118 mean_eta_119 mean_eta_120 mean_eta_121 mean_eta_122 mean_eta_123 mean_eta_124 mean_eta_125 mean_eta_126 mean_eta_127 mean_eta_128 mean_eta_129 mean_eta_130 mean_eta_131 mean_eta_132 mean_eta_133 mean_eta_134 mean_eta_135 mean_eta_136 mean_eta_137 mean_eta_138 mean_eta_139 mean_eta_140 mean_eta_141 mean_eta_142 mean_eta_143 mean_eta_144 mean_eta_145 mean_eta_146 mean_eta_147 mean_eta_148 mean_eta_149 mean_eta_150 mean_eta_151 mean_eta_152 mean_eta_153 mean_eta_154 mean_eta_155 mean_eta_156 mean_eta_157 mean_eta_158 mean_eta_159 mean_eta_160 mean_eta_161 mean_eta_162 mean_eta_163 mean_eta_164 mean_eta_165 mean_eta_166 mean_eta_167 mean_eta_168 mean_eta_169 mean_eta_170 mean_eta_171 mean_eta_172 mean_eta_173 mean_eta_174 mean_eta_175 mean_eta_176 mean_eta_177 mean_eta_178 mean_eta_179 mean_eta_180 mean_eta_181 mean_eta_182 mean_eta_183 mean_eta_184 mean_eta_185 mean_eta_186 mean_eta_187 mean_eta_188 mean_eta_189 mean_eta_190 mean_eta_191 mean_eta_192 mean_eta_193 mean_eta_194 mean_eta_195 mean_eta_196 mean_eta_197 mean_eta_198 mean_eta_199 mean_eta_200 mean_eta_201 mean_eta_202 mean_eta_203 mean_eta_204 mean_eta_205 mean_eta_206 mean_eta_207 mean_eta_208 mean_eta_209 mean_eta_210 mean_eta_211 mean_eta_212 mean_eta_213 mean_eta_214 mean_eta_215 mean_eta_216 mean_eta_217 mean_eta_218 mean_eta_219 mean_eta_220 mean_eta_221 mean_eta_222 mean_eta_223 mean_eta_224 mean_eta_225 mean_eta_226 mean_eta_227 mean_eta_228 mean_eta_229 mean_eta_230 mean_eta_231 mean_eta_232 mean_eta_233 mean_eta_234 mean_eta_235 mean_eta_236 mean_eta_237 mean_eta_238 mean_eta_239 mean_eta_240 mean_eta_241 mean_eta_242 mean_eta_243 mean_eta_244 mean_eta_245 mean_eta_246 mean_eta_247 mean_eta_248 mean_eta_249 mean_eta_250 mean_eta_251 mean_eta_252 mean_eta_253 mean_eta_254 mean_eta_255 mean_eta_256 mean_eta_257 mean_eta_258 mean_eta_259 mean_eta_260 mean_eta_261 mean_eta_262 mean_eta_263 mean_eta_264 mean_eta_265 mean_eta_266 mean_eta_267 mean_eta_268 mean_eta_269 mean_eta_270 mean_eta_271 mean_eta_272 mean_eta_273 mean_eta_274 mean_eta_275 mean_eta_276 mean_eta_277 mean_eta_278 mean_eta_279 mean_eta_280 mean_eta_281 mean_eta_282 mean_eta_283 mean_eta_284 mean_eta_285 mean_eta_286 mean_eta_287 mean_eta_288 mean_eta_289 mean_eta_290 mean_eta_291 mean_eta_292 mean_eta_293 mean_eta_294 mean_eta_295 mean_eta_296 mean_eta_297 mean_eta_298 mean_eta_299 mean_eta_300 mean_eta_301 mean_eta_302 mean_eta_303 mean_eta_304 mean_eta_305 mean_eta_306 mean_eta_307 mean_eta_308 mean_eta_309 mean_eta_310 mean_eta_311 mean_eta_312 mean_eta_313 mean_eta_314 mean_eta_315 mean_eta_316 mean_eta_317 mean_eta_318 mean_eta_319 mean_eta_320 mean_eta_321 mean_eta_322 mean_eta_323 mean_eta_324 mean_eta_325 mean_eta_326 mean_eta_327 mean_eta_328 mean_eta_329 mean_eta_330 mean_eta_331 mean_eta_332 mean_eta_333 mean_eta_334 mean_eta_335 mean_eta_336 mean_eta_337 mean_eta_338 mean_eta_339 mean_eta_340 mean_eta_341 mean_eta_342 mean_eta_343 mean_eta_344 mean_eta_345 mean_eta_346 mean_eta_347 mean_eta_348 mean_eta_349 mean_eta_350 mean_eta_351 mean_eta_352 mean_eta_353 mean_eta_354 mean_eta_355 mean_eta_356 mean_eta_357 mean_eta_358 mean_eta_359 mean_eta_360 mean_eta_361 mean_eta_362 mean_eta_363 mean_eta_364 mean_eta_365 mean_eta_366 mean_eta_367 mean_eta_368 mean_eta_369 mean_eta_370 mean_eta_371 mean_eta_372 mean_eta_373 mean_eta_374 mean_eta_375 mean_eta_376 mean_eta_377 mean_eta_378 mean_eta_379 mean_eta_380 mean_eta_381 mean_eta_382 mean_eta_383 mean_eta_384 mean_eta_385 mean_eta_386 mean_eta_387 mean_eta_388 mean_eta_389 mean_eta_390 mean_eta_391 mean_eta_392 mean_eta_393 mean_eta_394 mean_eta_395 mean_eta_396 mean_eta_397 mean_eta_398 mean_eta_399 mean_eta_400 mean_eta_401 mean_eta_402 mean_eta_403 mean_eta_404 mean_eta_405
_____ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________
1 0 1.7799 1.5892 1.4186 1.2663 1.1303 1.0091 0.90098 0.80474 0.71908 0.64289 0.57515 0.51497 0.46151 0.41406 0.37195 0.3346 0.30149 0.27214 0.24614 0.2231 0.20271 0.18466 0.1687 0.15458 0.14211 0.13083 0.11934 0.11052 0.10302 0.096388 0.090368 0.083527 0.078494 0.074464 0.071 0.067934 0.065304 0.06286 0.060435 0.058868 0.057496 0.056289 0.055211 0.054209 0.053257 0.052559 0.051949 0.051409 0.050921 0.050413 0.050032 0.049705 0.049418 0.049153 0.048923 0.048728 0.048527 0.048372 0.048234 0.048102 0.047992 0.047884 0.047791 0.047707 0.047627 0.047557 0.04749 0.047433 0.04738 0.047332 0.047289 0.047249 0.047212 0.047178 0.047148 0.047119 0.047095 0.047071 0.047051 0.047034 0.047018 1.3215 1.1869 1.0662 0.95802 0.86108 0.77424 0.69647 0.62683 0.56449 0.50869 0.45876 0.41408 0.37411 0.33836 0.3064 0.27783 0.25229 0.22947 0.20909 0.19089 0.17465 0.16016 0.14723 0.13572 0.12546 0.11611 0.10644 0.099005 0.09265 0.086997 0.081819 0.075843 0.071437 0.067899 0.064832 0.062111 0.059759 0.057557 0.055355 0.053932 0.052686 0.05158 0.050586 0.049662 0.048774 0.048123 0.047551 0.047042 0.046584 0.046101 0.045737 0.045427 0.045152 0.044897 0.044675 0.044485 0.044292 0.044142 0.044006 0.043877 0.043769 0.043664 0.043573 0.043491 0.043411 0.043343 0.043276 0.04322 0.043167 0.04312 0.043077 0.043037 0.043 0.042967 0.042936 0.042908 0.042884 0.04286 0.04284 0.042823 0.042807 1.0306 0.9189 0.81964 0.73149 0.65323 0.58381 0.52224 0.46763 0.41921 0.37626 0.33817 0.30437 0.27439 0.24778 0.22416 0.20319 0.18457 0.16803 0.15333 0.14026 0.12863 0.1183 0.10911 0.10094 0.093673 0.087047 0.080157 0.074882 0.070361 0.066329 0.062623 0.058292 0.055108 0.052538 0.050297 0.048295 0.04655 0.044906 0.043233 0.042159 0.041209 0.040356 0.039584 0.038857 0.03815 0.03763 0.037167 0.036751 0.036372 0.035971 0.035664 0.035397 0.03516 0.034939 0.034742 0.034575 0.034402 0.034265 0.034144 0.034024 0.033924 0.033827 0.033741 0.033665 0.033588 0.033525 0.033459 0.033407 0.033354 0.033309 0.033266 0.033228 0.033192 0.033159 0.033129 0.033101 0.033077 0.033054 0.033034 0.033017 0.033001 0.9771 0.86747 0.77031 0.68423 0.608 0.54055 0.48089 0.42815 0.38153 0.34034 0.30395 0.27179 0.24339 0.21831 0.19615 0.17658 0.1593 0.14403 0.13055 0.11864 0.10811 0.098821 0.090613 0.083374 0.076981 0.071211 0.065355 0.060855 0.05701 0.05361 0.050525 0.04704 0.044466 0.042388 0.040581 0.038983 0.037599 0.036314 0.035032 0.034179 0.033427 0.032753 0.032147 0.031581 0.031033 0.030618 0.030248 0.029914 0.02961 0.029297 0.029045 0.028827 0.028629 0.028445 0.02828 0.028136 0.02799 0.027871 0.027764 0.027658 0.02757 0.027482 0.027406 0.027338 0.027267 0.02721 0.02715 0.027101 0.027053 0.02701 0.026971 0.026935 0.026901 0.02687 0.026841 0.026815 0.026792 0.026771 0.026751 0.026735 0.02672 0.96453 0.8552 0.75836 0.67262 0.59674 0.52964 0.47033 0.41793 0.37164 0.33077 0.29469 0.26285 0.23475 0.20995 0.18808 0.16878 0.15176 0.13675 0.12351 0.11183 0.10153 0.092458 0.084459 0.077418 0.071214 0.065627 0.059962 0.05563 0.051941 0.048689 0.045742 0.042406 0.03996 0.037996 0.036299 0.0348 0.03351 0.032308 0.031119 0.030335 0.029647 0.02903 0.02848 0.027966 0.027472 0.027102 0.02677 0.026472 0.026202 0.025923 0.025701 0.02551 0.025337 0.025176 0.02503 0.024906 0.02478 0.024677 0.024583 0.02449 0.024414 0.024339 0.024271 0.024211 0.024149 0.0241 0.024047 0.024005 0.023962 0.023925 0.023891 0.023859 0.023828 0.023802 0.023776 0.023753 0.023734 0.023714 0.023697 0.023683 0.023669
2 0.00051498 1.7463 1.561 1.3951 1.2466 1.1138 0.99527 0.88946 0.79511 0.71103 0.63616 0.56953 0.51026 0.45757 0.41076 0.36919 0.33228 0.29954 0.2705 0.24475 0.22193 0.20171 0.18381 0.16797 0.15396 0.14157 0.13036 0.11893 0.11016 0.1027 0.096104 0.090112 0.083299 0.078286 0.074271 0.070819 0.067763 0.065141 0.062704 0.060286 0.058722 0.057353 0.056149 0.055073 0.054072 0.053122 0.052425 0.051816 0.051276 0.050789 0.050282 0.049901 0.049574 0.049287 0.049022 0.048792 0.048597 0.048397 0.048242 0.048103 0.047972 0.047861 0.047753 0.047661 0.047577 0.047497 0.047427 0.04736 0.047303 0.04725 0.047202 0.047159 0.047119 0.047082 0.047048 0.047018 0.046989 0.046965 0.046941 0.046921 0.046904 0.046888 1.2979 1.1671 1.0496 0.94411 0.84939 0.76442 0.68821 0.61988 0.55865 0.50377 0.45461 0.41058 0.37116 0.33587 0.30429 0.27604 0.25078 0.22819 0.208 0.18996 0.17385 0.15947 0.14664 0.13521 0.12502 0.11572 0.1061 0.098702 0.092379 0.086752 0.081597 0.075645 0.071256 0.067731 0.064674 0.061961 0.059616 0.05742 0.055224 0.053804 0.052561 0.051457 0.050465 0.049542 0.048656 0.048006 0.047434 0.046926 0.046468 0.045986 0.045623 0.045312 0.045037 0.044783 0.044561 0.044371 0.044178 0.044028 0.043892 0.043764 0.043655 0.043551 0.043459 0.043377 0.043297 0.04323 0.043163 0.043107 0.043054 0.043006 0.042964 0.042923 0.042887 0.042853 0.042823 0.042794 0.04277 0.042747 0.042727 0.042709 0.042694 1.0109 0.9025 0.80604 0.7202 0.64386 0.57602 0.51576 0.46225 0.41472 0.37253 0.33505 0.30177 0.27222 0.24596 0.22264 0.20191 0.18349 0.16712 0.15256 0.1396 0.12807 0.11782 0.1087 0.10058 0.093363 0.086776 0.079921 0.074673 0.070173 0.06616 0.062469 0.058155 0.054983 0.052421 0.050187 0.048191 0.046451 0.044811 0.043142 0.04207 0.041122 0.04027 0.0395 0.038774 0.038068 0.037548 0.037085 0.03667 0.036291 0.035891 0.035584 0.035317 0.03508 0.03486 0.034663 0.034496 0.034323 0.034186 0.034065 0.033945 0.033845 0.033748 0.033662 0.033586 0.033509 0.033446 0.03338 0.033328 0.033275 0.03323 0.033188 0.033149 0.033113 0.033081 0.033051 0.033022 0.032999 0.032976 0.032955 0.032939 0.032922 0.9576 0.85132 0.75695 0.67315 0.59882 0.53294 0.47459 0.42292 0.3772 0.33674 0.30096 0.26931 0.24133 0.21659 0.19472 0.17539 0.1583 0.1432 0.12985 0.11805 0.10762 0.098398 0.090253 0.083065 0.076715 0.070981 0.065157 0.060681 0.056855 0.053472 0.0504 0.046929 0.044365 0.042294 0.040492 0.0389 0.037519 0.036237 0.034959 0.034107 0.033357 0.032684 0.032079 0.031513 0.030966 0.030552 0.030182 0.029848 0.029545 0.029232 0.028981 0.028762 0.028565 0.028381 0.028215 0.028072 0.027925 0.027806 0.0277 0.027594 0.027506 0.027418 0.027342 0.027273 0.027203 0.027146 0.027085 0.027037 0.026989 0.026946 0.026907 0.026871 0.026836 0.026806 0.026777 0.026751 0.026728 0.026707 0.026687 0.026671 0.026656 0.94504 0.83906 0.745 0.66155 0.58757 0.52204 0.46403 0.41271 0.36731 0.32718 0.29172 0.26038 0.23269 0.20824 0.18666 0.1676 0.15077 0.13592 0.12281 0.11124 0.10104 0.092038 0.084102 0.077112 0.070951 0.065399 0.059766 0.055458 0.051789 0.048552 0.045619 0.042297 0.039861 0.037904 0.036212 0.034719 0.033432 0.032233 0.031048 0.030266 0.029578 0.028963 0.028414 0.027901 0.027407 0.027037 0.026706 0.026408 0.026139 0.025859 0.025638 0.025447 0.025275 0.025113 0.024967 0.024844 0.024717 0.024614 0.02452 0.024427 0.024352 0.024276 0.024208 0.024148 0.024087 0.024038 0.023984 0.023942 0.023899 0.023862 0.023828 0.023796 0.023766 0.02374 0.023714 0.02369 0.023671 0.023651 0.023634 0.02362 0.023607
3 0.0041199 1.2806 1.1656 1.0586 0.96002 0.86971 0.78739 0.71257 0.64471 0.58324 0.52764 0.47741 0.43209 0.39125 0.35447 0.3214 0.29169 0.26502 0.24111 0.21968 0.2005 0.18335 0.16803 0.15436 0.14216 0.13129 0.12138 0.11115 0.10328 0.096545 0.090569 0.085099 0.07883 0.074198 0.070472 0.067249 0.064392 0.061928 0.05962 0.057336 0.055845 0.054535 0.053378 0.052342 0.051378 0.050456 0.04978 0.049189 0.048659 0.048185 0.04769 0.047314 0.046994 0.046711 0.04645 0.046222 0.046031 0.045832 0.045679 0.045541 0.045411 0.045301 0.045193 0.045102 0.045018 0.044938 0.04487 0.044802 0.044746 0.044692 0.044645 0.044602 0.044562 0.044525 0.044491 0.044461 0.044432 0.044408 0.044385 0.044364 0.044347 0.044331 0.97308 0.89106 0.81411 0.74264 0.67684 0.61659 0.56159 0.51146 0.46583 0.42433 0.38663 0.35241 0.32137 0.29324 0.26777 0.24473 0.2239 0.20509 0.18811 0.1728 0.15901 0.1466 0.13544 0.12542 0.11643 0.10816 0.099491 0.092829 0.087097 0.081977 0.077254 0.071756 0.067689 0.06441 0.061546 0.059007 0.056795 0.054715 0.052636 0.051281 0.050089 0.049027 0.048072 0.047181 0.046319 0.04569 0.045134 0.044634 0.04419 0.043718 0.043359 0.043055 0.042784 0.042533 0.042313 0.042126 0.041936 0.041786 0.041652 0.041524 0.041416 0.041312 0.041222 0.04114 0.041061 0.040993 0.040926 0.040871 0.040818 0.04077 0.040728 0.040688 0.040651 0.040618 0.040588 0.040559 0.040535 0.040512 0.040491 0.040474 0.040458 0.74704 0.68105 0.61925 0.56211 0.50984 0.46235 0.41935 0.38048 0.34539 0.31373 0.28519 0.25948 0.23632 0.21547 0.19671 0.17983 0.16465 0.15101 0.13875 0.12773 0.11784 0.10897 0.101 0.093867 0.08747 0.081588 0.075393 0.070646 0.066548 0.062882 0.059482 0.055481 0.052529 0.050134 0.048031 0.046153 0.044505 0.042942 0.041357 0.04033 0.039416 0.038593 0.037848 0.037144 0.036456 0.03595 0.035498 0.035088 0.034719 0.034327 0.034023 0.033761 0.033527 0.033308 0.033113 0.032949 0.032778 0.032641 0.032521 0.032402 0.032303 0.032205 0.03212 0.032044 0.031968 0.031905 0.031839 0.031788 0.031735 0.03169 0.031647 0.031609 0.031573 0.03154 0.03151 0.031482 0.031458 0.031435 0.031415 0.031399 0.031382 0.69871 0.63453 0.57454 0.51919 0.46867 0.42288 0.38154 0.3443 0.31078 0.28066 0.25361 0.22934 0.20757 0.18808 0.17062 0.155 0.14104 0.12855 0.1174 0.10744 0.098551 0.090631 0.083573 0.077292 0.071701 0.06661 0.061371 0.057341 0.05387 0.050793 0.047975 0.044766 0.042389 0.040459 0.038762 0.037267 0.03596 0.03474 0.033526 0.032709 0.031985 0.031334 0.030749 0.0302 0.029665 0.029262 0.0289 0.028569 0.028273 0.027966 0.027716 0.027501 0.027306 0.027122 0.026958 0.026817 0.026672 0.026553 0.026447 0.026342 0.026254 0.026166 0.02609 0.026022 0.025952 0.025895 0.025835 0.025787 0.025738 0.025696 0.025656 0.025621 0.025586 0.025556 0.025527 0.025501 0.025478 0.025457 0.025437 0.025421 0.025406 0.68632 0.62242 0.56275 0.50773 0.45756 0.41212 0.37112 0.3342 0.30102 0.27121 0.24447 0.2205 0.19904 0.17982 0.16265 0.14729 0.13358 0.12135 0.11044 0.10071 0.092046 0.08434 0.077486 0.0714 0.065995 0.061083 0.056034 0.052171 0.048855 0.045921 0.043239 0.040179 0.037927 0.03611 0.034523 0.033126 0.031912 0.030773 0.029651 0.028904 0.028242 0.027648 0.027119 0.026621 0.02614 0.02578 0.025456 0.025162 0.024899 0.024625 0.024405 0.024217 0.024047 0.023886 0.023741 0.02362 0.023494 0.023391 0.023298 0.023205 0.02313 0.023054 0.022987 0.022928 0.022866 0.022817 0.022764 0.022722 0.022679 0.022642 0.022608 0.022576 0.022546 0.022519 0.022494 0.022471 0.022451 0.022431 0.022414 0.0224 0.022387
4 0.013905 0.80413 0.74652 0.69063 0.63731 0.58715 0.54037 0.49695 0.4568 0.41973 0.38555 0.35408 0.32513 0.29855 0.27418 0.25187 0.23147 0.21285 0.19589 0.18044 0.16641 0.15367 0.14214 0.1317 0.12227 0.11376 0.10588 0.097587 0.091184 0.085655 0.080696 0.076108 0.070802 0.066828 0.063606 0.060797 0.058289 0.056111 0.054043 0.052005 0.050647 0.049442 0.048377 0.047418 0.046521 0.045658 0.04502 0.044461 0.043953 0.043504 0.043032 0.042668 0.042359 0.042087 0.041833 0.041611 0.041424 0.04123 0.041079 0.040944 0.040816 0.040707 0.0406 0.040511 0.040427 0.040349 0.040281 0.040214 0.040158 0.040104 0.040057 0.040015 0.039975 0.039938 0.039905 0.039874 0.039846 0.039821 0.039798 0.039778 0.039761 0.039745 0.64049 0.59723 0.55476 0.51393 0.47535 0.43928 0.40575 0.37468 0.34593 0.31934 0.29478 0.2721 0.25119 0.23194 0.21422 0.19795 0.18302 0.16934 0.15681 0.14537 0.13493 0.12541 0.11676 0.10889 0.10175 0.095089 0.087983 0.082501 0.077749 0.073462 0.069467 0.064779 0.061271 0.058411 0.055909 0.053667 0.051701 0.049832 0.047968 0.046732 0.045631 0.044649 0.043765 0.042933 0.042123 0.041531 0.041003 0.040524 0.040101 0.039652 0.039305 0.039009 0.038749 0.038504 0.038289 0.038107 0.037921 0.037774 0.037641 0.037516 0.037409 0.037306 0.037217 0.037136 0.037058 0.036991 0.036925 0.03687 0.036816 0.036769 0.036727 0.036687 0.036651 0.036618 0.036588 0.036559 0.036535 0.036512 0.036492 0.036475 0.036459 0.49336 0.45891 0.42487 0.39214 0.36133 0.33271 0.3063 0.28201 0.25972 0.23926 0.22049 0.20328 0.18751 0.17308 0.15988 0.14782 0.13681 0.12676 0.1176 0.10925 0.10166 0.094756 0.088491 0.082803 0.077645 0.072836 0.067693 0.063726 0.060279 0.057163 0.054248 0.050806 0.048227 0.046114 0.044255 0.042572 0.041092 0.039671 0.038236 0.037288 0.036432 0.035663 0.034967 0.034304 0.033652 0.03317 0.032739 0.032343 0.031988 0.031613 0.031316 0.031061 0.030835 0.03062 0.030429 0.030269 0.0301 0.029965 0.029846 0.029729 0.02963 0.029533 0.02945 0.029374 0.029298 0.029236 0.029171 0.02912 0.029067 0.029022 0.02898 0.028942 0.028906 0.028874 0.028844 0.028816 0.028791 0.028769 0.028749 0.028732 0.028716 0.45321 0.42015 0.38752 0.35617 0.32672 0.29942 0.27431 0.2513 0.23025 0.211 0.19341 0.17735 0.1627 0.14935 0.1372 0.12616 0.11612 0.10701 0.098758 0.091282 0.084523 0.078419 0.072917 0.067956 0.063489 0.059363 0.055056 0.051712 0.048811 0.046211 0.043804 0.04106 0.038989 0.037287 0.035789 0.03445 0.033276 0.032165 0.031065 0.030312 0.02963 0.029021 0.028471 0.02795 0.027442 0.027057 0.02671 0.026389 0.026102 0.025808 0.025562 0.02535 0.025163 0.024981 0.024819 0.024681 0.024538 0.02442 0.024315 0.024211 0.024124 0.024036 0.023961 0.023893 0.023824 0.023767 0.023707 0.023659 0.02361 0.023569 0.023529 0.023494 0.02346 0.023429 0.023401 0.023374 0.023351 0.02333 0.023311 0.023295 0.02328 0.44115 0.40836 0.37603 0.345 0.31589 0.28893 0.26414 0.24145 0.22072 0.20178 0.1845 0.16873 0.15437 0.1413 0.12942 0.11863 0.10885 0.099982 0.091959 0.08471 0.078169 0.072274 0.06697 0.062196 0.057907 0.053955 0.049831 0.046651 0.043899 0.041439 0.039165 0.036564 0.034617 0.033028 0.031636 0.030393 0.02931 0.02828 0.027268 0.026582 0.025962 0.025409 0.024914 0.024445 0.023989 0.023646 0.023336 0.02305 0.022797 0.022534 0.022319 0.022134 0.02197 0.021811 0.021668 0.02155 0.021426 0.021323 0.021231 0.021139 0.021065 0.020989 0.020923 0.020863 0.020803 0.020754 0.020701 0.020659 0.020616 0.020579 0.020545 0.020513 0.020483 0.020457 0.020431 0.020408 0.020388 0.020369 0.020352 0.020338 0.020325
5 0.032959 0.49946 0.47014 0.43986 0.40984 0.38099 0.35375 0.32834 0.30478 0.28299 0.26285 0.2442 0.22696 0.21102 0.19627 0.18261 0.16998 0.1583 0.1475 0.13754 0.12836 0.1199 0.11213 0.10498 0.098426 0.092419 0.086776 0.080683 0.075951 0.071821 0.06806 0.064541 0.060436 0.057293 0.054699 0.052429 0.050384 0.048562 0.046838 0.04513 0.043947 0.042894 0.041961 0.041108 0.040301 0.03953 0.03895 0.038431 0.037965 0.037549 0.037111 0.036769 0.036475 0.03622 0.035977 0.035765 0.035583 0.035398 0.035253 0.035118 0.034996 0.034888 0.034784 0.034697 0.034614 0.034537 0.03447 0.034405 0.03435 0.034296 0.034249 0.034207 0.034167 0.034131 0.034098 0.034068 0.03404 0.034015 0.033993 0.033972 0.033955 0.03394 0.42622 0.40186 0.37633 0.35086 0.32634 0.30323 0.28174 0.26189 0.24359 0.22672 0.21114 0.19676 0.18347 0.17118 0.1598 0.14925 0.13949 0.13044 0.12208 0.11434 0.10719 0.1006 0.094514 0.08891 0.083755 0.078889 0.07358 0.069461 0.065854 0.062554 0.059447 0.055784 0.052978 0.050652 0.048619 0.046766 0.045116 0.043545 0.041973 0.040893 0.039923 0.039058 0.038271 0.037513 0.036793 0.03625 0.035759 0.03532 0.034924 0.034508 0.034181 0.033898 0.033652 0.03342 0.033214 0.033037 0.032858 0.032716 0.032585 0.032466 0.032359 0.032258 0.032172 0.032091 0.032015 0.03195 0.031885 0.03183 0.031777 0.03173 0.031687 0.031648 0.031613 0.03158 0.03155 0.031522 0.031497 0.031474 0.031454 0.031437 0.031421 0.34585 0.32527 0.30338 0.28144 0.26038 0.24067 0.22249 0.20589 0.19075 0.17695 0.16433 0.1528 0.14226 0.13259 0.12371 0.11554 0.10802 0.1011 0.09473 0.088868 0.083479 0.078521 0.073964 0.069773 0.065926 0.062302 0.058358 0.055289 0.052594 0.050123 0.047796 0.045053 0.042941 0.041179 0.039626 0.038206 0.03693 0.03571 0.034481 0.033629 0.032857 0.032168 0.031533 0.03092 0.030332 0.029882 0.029475 0.029105 0.02877 0.028418 0.028134 0.027887 0.027673 0.027466 0.027282 0.027124 0.026961 0.02683 0.026711 0.026598 0.026501 0.026405 0.026323 0.026247 0.026173 0.026112 0.026048 0.025997 0.025944 0.0259 0.025857 0.02582 0.025784 0.025752 0.025722 0.025695 0.02567 0.025648 0.025628 0.025612 0.025596 0.31453 0.295 0.27419 0.25334 0.23335 0.21465 0.19747 0.18181 0.16758 0.15466 0.1429 0.1322 0.12246 0.11357 0.10544 0.098009 0.091203 0.084976 0.079277 0.074063 0.069297 0.06494 0.06096 0.057325 0.054007 0.050911 0.04763 0.045055 0.042797 0.040738 0.038818 0.036642 0.034944 0.03352 0.03227 0.031135 0.030117 0.029159 0.028213 0.02753 0.02691 0.02636 0.025853 0.025366 0.024907 0.024543 0.024212 0.02391 0.023636 0.023358 0.023121 0.022915 0.022735 0.02256 0.022403 0.022267 0.022126 0.022012 0.021906 0.021806 0.021719 0.021631 0.021558 0.02149 0.021423 0.021366 0.021307 0.02126 0.02121 0.021169 0.021129 0.021094 0.02106 0.02103 0.021001 0.020976 0.020952 0.020932 0.020912 0.020897 0.020882 0.30302 0.28374 0.26322 0.24268 0.22299 0.20461 0.18773 0.17238 0.15845 0.14582 0.13435 0.12392 0.11445 0.10583 0.097956 0.09077 0.084207 0.078213 0.072738 0.067741 0.063182 0.059022 0.05523 0.051774 0.048628 0.045699 0.042593 0.04017 0.038052 0.036128 0.034336 0.032297 0.03072 0.029405 0.028255 0.027215 0.026282 0.025405 0.024542 0.023924 0.023364 0.022871 0.022418 0.021981 0.021571 0.021247 0.020954 0.020686 0.020444 0.020197 0.019989 0.01981 0.019653 0.019498 0.01936 0.019243 0.019122 0.019022 0.018929 0.018841 0.018766 0.018691 0.018626 0.018566 0.018507 0.018459 0.018407 0.018365 0.018321 0.018285 0.018251 0.018219 0.01819 0.018164 0.018138 0.018115 0.018095 0.018076 0.018059 0.018046 0.018032
6 0.064373 0.33366 0.31704 0.29894 0.2803 0.26191 0.2443 0.22768 0.21211 0.19762 0.18419 0.17179 0.16033 0.14972 0.13992 0.13091 0.12261 0.11496 0.10791 0.1014 0.095407 0.089874 0.084773 0.080066 0.07572 0.071708 0.067903 0.063758 0.060497 0.057604 0.054943 0.052427 0.049493 0.047165 0.045224 0.043502 0.04191 0.0405 0.039136 0.037781 0.036802 0.035931 0.03514 0.034408 0.033719 0.033059 0.032542 0.032076 0.031668 0.031288 0.030889 0.030584 0.030311 0.030067 0.029846 0.029648 0.02947 0.029297 0.02916 0.029028 0.028913 0.028808 0.028707 0.028623 0.028539 0.028468 0.028402 0.028338 0.028284 0.028231 0.028185 0.028141 0.028103 0.028067 0.028034 0.028004 0.027977 0.027952 0.02793 0.02791 0.027893 0.027877 0.30477 0.28957 0.27283 0.25552 0...
% Consumption
st_title = ['MEAN(MN_MPC_U_GAIN_CHECK(A,Z)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_c = ff_summ_nd_array(st_title, mn_MPC_U_gain_share_check, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_MPC_U_GAIN_CHECK(A,Z)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15 mean_eta_16 mean_eta_17 mean_eta_18 mean_eta_19 mean_eta_20 mean_eta_21 mean_eta_22 mean_eta_23 mean_eta_24 mean_eta_25 mean_eta_26 mean_eta_27 mean_eta_28 mean_eta_29 mean_eta_30 mean_eta_31 mean_eta_32 mean_eta_33 mean_eta_34 mean_eta_35 mean_eta_36 mean_eta_37 mean_eta_38 mean_eta_39 mean_eta_40 mean_eta_41 mean_eta_42 mean_eta_43 mean_eta_44 mean_eta_45 mean_eta_46 mean_eta_47 mean_eta_48 mean_eta_49 mean_eta_50 mean_eta_51 mean_eta_52 mean_eta_53 mean_eta_54 mean_eta_55 mean_eta_56 mean_eta_57 mean_eta_58 mean_eta_59 mean_eta_60 mean_eta_61 mean_eta_62 mean_eta_63 mean_eta_64 mean_eta_65 mean_eta_66 mean_eta_67 mean_eta_68 mean_eta_69 mean_eta_70 mean_eta_71 mean_eta_72 mean_eta_73 mean_eta_74 mean_eta_75 mean_eta_76 mean_eta_77 mean_eta_78 mean_eta_79 mean_eta_80 mean_eta_81 mean_eta_82 mean_eta_83 mean_eta_84 mean_eta_85 mean_eta_86 mean_eta_87 mean_eta_88 mean_eta_89 mean_eta_90 mean_eta_91 mean_eta_92 mean_eta_93 mean_eta_94 mean_eta_95 mean_eta_96 mean_eta_97 mean_eta_98 mean_eta_99 mean_eta_100 mean_eta_101 mean_eta_102 mean_eta_103 mean_eta_104 mean_eta_105 mean_eta_106 mean_eta_107 mean_eta_108 mean_eta_109 mean_eta_110 mean_eta_111 mean_eta_112 mean_eta_113 mean_eta_114 mean_eta_115 mean_eta_116 mean_eta_117 mean_eta_118 mean_eta_119 mean_eta_120 mean_eta_121 mean_eta_122 mean_eta_123 mean_eta_124 mean_eta_125 mean_eta_126 mean_eta_127 mean_eta_128 mean_eta_129 mean_eta_130 mean_eta_131 mean_eta_132 mean_eta_133 mean_eta_134 mean_eta_135 mean_eta_136 mean_eta_137 mean_eta_138 mean_eta_139 mean_eta_140 mean_eta_141 mean_eta_142 mean_eta_143 mean_eta_144 mean_eta_145 mean_eta_146 mean_eta_147 mean_eta_148 mean_eta_149 mean_eta_150 mean_eta_151 mean_eta_152 mean_eta_153 mean_eta_154 mean_eta_155 mean_eta_156 mean_eta_157 mean_eta_158 mean_eta_159 mean_eta_160 mean_eta_161 mean_eta_162 mean_eta_163 mean_eta_164 mean_eta_165 mean_eta_166 mean_eta_167 mean_eta_168 mean_eta_169 mean_eta_170 mean_eta_171 mean_eta_172 mean_eta_173 mean_eta_174 mean_eta_175 mean_eta_176 mean_eta_177 mean_eta_178 mean_eta_179 mean_eta_180 mean_eta_181 mean_eta_182 mean_eta_183 mean_eta_184 mean_eta_185 mean_eta_186 mean_eta_187 mean_eta_188 mean_eta_189 mean_eta_190 mean_eta_191 mean_eta_192 mean_eta_193 mean_eta_194 mean_eta_195 mean_eta_196 mean_eta_197 mean_eta_198 mean_eta_199 mean_eta_200 mean_eta_201 mean_eta_202 mean_eta_203 mean_eta_204 mean_eta_205 mean_eta_206 mean_eta_207 mean_eta_208 mean_eta_209 mean_eta_210 mean_eta_211 mean_eta_212 mean_eta_213 mean_eta_214 mean_eta_215 mean_eta_216 mean_eta_217 mean_eta_218 mean_eta_219 mean_eta_220 mean_eta_221 mean_eta_222 mean_eta_223 mean_eta_224 mean_eta_225 mean_eta_226 mean_eta_227 mean_eta_228 mean_eta_229 mean_eta_230 mean_eta_231 mean_eta_232 mean_eta_233 mean_eta_234 mean_eta_235 mean_eta_236 mean_eta_237 mean_eta_238 mean_eta_239 mean_eta_240 mean_eta_241 mean_eta_242 mean_eta_243 mean_eta_244 mean_eta_245 mean_eta_246 mean_eta_247 mean_eta_248 mean_eta_249 mean_eta_250 mean_eta_251 mean_eta_252 mean_eta_253 mean_eta_254 mean_eta_255 mean_eta_256 mean_eta_257 mean_eta_258 mean_eta_259 mean_eta_260 mean_eta_261 mean_eta_262 mean_eta_263 mean_eta_264 mean_eta_265 mean_eta_266 mean_eta_267 mean_eta_268 mean_eta_269 mean_eta_270 mean_eta_271 mean_eta_272 mean_eta_273 mean_eta_274 mean_eta_275 mean_eta_276 mean_eta_277 mean_eta_278 mean_eta_279 mean_eta_280 mean_eta_281 mean_eta_282 mean_eta_283 mean_eta_284 mean_eta_285 mean_eta_286 mean_eta_287 mean_eta_288 mean_eta_289 mean_eta_290 mean_eta_291 mean_eta_292 mean_eta_293 mean_eta_294 mean_eta_295 mean_eta_296 mean_eta_297 mean_eta_298 mean_eta_299 mean_eta_300 mean_eta_301 mean_eta_302 mean_eta_303 mean_eta_304 mean_eta_305 mean_eta_306 mean_eta_307 mean_eta_308 mean_eta_309 mean_eta_310 mean_eta_311 mean_eta_312 mean_eta_313 mean_eta_314 mean_eta_315 mean_eta_316 mean_eta_317 mean_eta_318 mean_eta_319 mean_eta_320 mean_eta_321 mean_eta_322 mean_eta_323 mean_eta_324 mean_eta_325 mean_eta_326 mean_eta_327 mean_eta_328 mean_eta_329 mean_eta_330 mean_eta_331 mean_eta_332 mean_eta_333 mean_eta_334 mean_eta_335 mean_eta_336 mean_eta_337 mean_eta_338 mean_eta_339 mean_eta_340 mean_eta_341 mean_eta_342 mean_eta_343 mean_eta_344 mean_eta_345 mean_eta_346 mean_eta_347 mean_eta_348 mean_eta_349 mean_eta_350 mean_eta_351 mean_eta_352 mean_eta_353 mean_eta_354 mean_eta_355 mean_eta_356 mean_eta_357 mean_eta_358 mean_eta_359 mean_eta_360 mean_eta_361 mean_eta_362 mean_eta_363 mean_eta_364 mean_eta_365 mean_eta_366 mean_eta_367 mean_eta_368 mean_eta_369 mean_eta_370 mean_eta_371 mean_eta_372 mean_eta_373 mean_eta_374 mean_eta_375 mean_eta_376 mean_eta_377 mean_eta_378 mean_eta_379 mean_eta_380 mean_eta_381 mean_eta_382 mean_eta_383 mean_eta_384 mean_eta_385 mean_eta_386 mean_eta_387 mean_eta_388 mean_eta_389 mean_eta_390 mean_eta_391 mean_eta_392 mean_eta_393 mean_eta_394 mean_eta_395 mean_eta_396 mean_eta_397 mean_eta_398 mean_eta_399 mean_eta_400 mean_eta_401 mean_eta_402 mean_eta_403 mean_eta_404 mean_eta_405
_____ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________
1 0 0.99875 0.99698 0.99542 0.99457 0.99452 0.99473 0.99494 0.99511 0.99524 0.99539 0.99563 0.99589 0.99615 0.99637 0.99647 0.99647 0.9964 0.99631 0.99615 0.99529 0.99435 0.99146 0.98966 0.98603 0.98259 0.98026 0.9762 0.97007 0.96701 0.95873 0.95483 0.94912 0.93895 0.93051 0.92829 0.92039 0.91214 0.90473 0.89065 0.88588 0.87462 0.86359 0.85277 0.83834 0.83318 0.81781 0.80503 0.79577 0.78333 0.76805 0.75938 0.74414 0.74348 0.72957 0.72319 0.70291 0.6953 0.69176 0.66776 0.66705 0.64364 0.63551 0.6248 0.60781 0.60208 0.58785 0.5778 0.56344 0.55614 0.54821 0.53592 0.53101 0.52241 0.52164 0.52296 0.52007 0.51171 0.50868 0.50013 0.50461 0.49216 0.9988 0.99704 0.99547 0.99462 0.99457 0.99478 0.99499 0.99516 0.99529 0.99543 0.99567 0.99594 0.99619 0.99642 0.99652 0.99651 0.99644 0.99635 0.99619 0.99532 0.99438 0.99149 0.98969 0.98606 0.98262 0.98029 0.97593 0.96936 0.96543 0.95834 0.95358 0.94941 0.93792 0.92779 0.92676 0.91591 0.9107 0.89977 0.889 0.88102 0.87125 0.85869 0.84889 0.8348 0.82766 0.81109 0.803 0.78958 0.77715 0.76199 0.75358 0.73927 0.73268 0.72087 0.71765 0.69727 0.68522 0.67757 0.6593 0.65557 0.63664 0.62735 0.61908 0.5992 0.59187 0.58077 0.56956 0.55799 0.5506 0.54045 0.53283 0.52551 0.52269 0.51353 0.51723 0.51813 0.51333 0.50808 0.50419 0.50337 0.49886 0.86307 0.8628 0.86143 0.86007 0.85918 0.8581 0.85634 0.85427 0.85141 0.84905 0.84746 0.84646 0.84596 0.84554 0.8455 0.84537 0.84835 0.84939 0.84967 0.84977 0.85114 0.8522 0.85256 0.84986 0.84457 0.84038 0.82515 0.81201 0.80891 0.80249 0.80341 0.79549 0.77601 0.76758 0.76479 0.75987 0.75301 0.74515 0.73103 0.72538 0.71887 0.70661 0.69941 0.68435 0.67257 0.65928 0.64886 0.64109 0.6333 0.6147 0.60908 0.59576 0.59354 0.58179 0.58211 0.55884 0.55062 0.55314 0.5374 0.53187 0.52227 0.51993 0.51161 0.50133 0.49618 0.495 0.4877 0.47881 0.478 0.47297 0.47021 0.46489 0.46406 0.46502 0.46755 0.46113 0.46115 0.45629 0.45011 0.45363 0.44803 0.62811 0.62987 0.63002 0.627 0.62637 0.62636 0.62781 0.62939 0.62869 0.62985 0.62973 0.63039 0.62926 0.62993 0.62734 0.62551 0.62292 0.62592 0.62744 0.62712 0.6267 0.62501 0.62305 0.6214 0.61506 0.61239 0.60921 0.60825 0.60571 0.59395 0.58988 0.58081 0.56875 0.56228 0.56459 0.55159 0.54818 0.5281 0.51906 0.50996 0.50686 0.49903 0.49816 0.48618 0.48314 0.47098 0.46134 0.45524 0.44784 0.43535 0.42762 0.42072 0.41646 0.41311 0.41025 0.40279 0.39476 0.39792 0.38204 0.38521 0.37513 0.37444 0.37534 0.3651 0.37055 0.36719 0.36753 0.35551 0.35925 0.35927 0.35613 0.35474 0.35249 0.35038 0.35831 0.3572 0.35267 0.34814 0.34207 0.34716 0.34087 0.57251 0.56756 0.5696 0.57059 0.56716 0.57062 0.56833 0.56875 0.56924 0.57056 0.57001 0.56984 0.5715 0.57005 0.56717 0.57045 0.57334 0.56606 0.57084 0.57097 0.56507 0.56498 0.56478 0.55952 0.55675 0.55606 0.54901 0.54171 0.54069 0.5273 0.53015 0.51888 0.51502 0.50373 0.50219 0.4954 0.48761 0.47944 0.47263 0.45899 0.45835 0.4484 0.43944 0.43643 0.43058 0.41871 0.40774 0.40307 0.39751 0.38453 0.37869 0.37046 0.37126 0.36805 0.37118 0.3527 0.35246 0.35854 0.33897 0.33898 0.33284 0.33605 0.34125 0.32761 0.32408 0.32779 0.32655 0.32033 0.32562 0.32663 0.32105 0.32291 0.31762 0.31319 0.32149 0.31903 0.31566 0.31601 0.31519 0.3171 0.31942
2 0.00051498 0.99851 0.99643 0.99458 0.99357 0.99352 0.99376 0.99401 0.99421 0.99437 0.99455 0.99486 0.99521 0.99557 0.9959 0.99609 0.99613 0.99606 0.9959 0.99563 0.99455 0.99366 0.99058 0.98905 0.98552 0.982 0.97951 0.97551 0.96946 0.96647 0.95843 0.95422 0.94854 0.93828 0.9297 0.9275 0.9198 0.91173 0.90408 0.89065 0.88498 0.87381 0.86277 0.85186 0.83829 0.83284 0.81758 0.80479 0.79543 0.78242 0.76802 0.75919 0.74369 0.74342 0.72942 0.72307 0.70297 0.69545 0.69135 0.66784 0.66761 0.64318 0.63484 0.62537 0.60768 0.60202 0.58741 0.57791 0.56326 0.55579 0.54823 0.53581 0.53038 0.52217 0.52146 0.52296 0.52026 0.51182 0.50888 0.49985 0.50444 0.49201 0.99857 0.99648 0.99463 0.99362 0.99357 0.99381 0.99406 0.99426 0.99442 0.9946 0.9949 0.99525 0.99561 0.99595 0.99613 0.99617 0.9961 0.99594 0.99567 0.99458 0.99369 0.99061 0.98908 0.98555 0.98203 0.97954 0.97525 0.96869 0.96491 0.95801 0.95301 0.94884 0.93722 0.92691 0.92613 0.91532 0.91044 0.89901 0.88915 0.88032 0.87013 0.85801 0.84794 0.83474 0.82719 0.81085 0.80272 0.78901 0.77621 0.76168 0.75298 0.73902 0.73263 0.72054 0.71734 0.69734 0.68556 0.67736 0.65931 0.65556 0.63676 0.62718 0.61936 0.59877 0.59189 0.58075 0.56921 0.55791 0.55011 0.54044 0.53249 0.52497 0.52288 0.51351 0.51725 0.51836 0.51353 0.50809 0.50373 0.50325 0.49869 0.86313 0.86222 0.86053 0.85914 0.85811 0.85721 0.85562 0.85357 0.85104 0.84814 0.84661 0.84544 0.84508 0.8447 0.84417 0.84427 0.8472 0.84834 0.84848 0.84831 0.84948 0.85073 0.85097 0.8491 0.84396 0.8399 0.82472 0.81098 0.80772 0.80115 0.80172 0.79468 0.77572 0.76547 0.76232 0.75777 0.75166 0.74378 0.73091 0.72453 0.71736 0.70542 0.69789 0.68346 0.67134 0.65876 0.64794 0.64027 0.63205 0.61378 0.60828 0.5946 0.59311 0.58068 0.58198 0.55827 0.55037 0.55236 0.53704 0.53161 0.52184 0.51919 0.51162 0.50087 0.49607 0.49444 0.48689 0.47804 0.47728 0.47305 0.46968 0.46405 0.46369 0.46468 0.46721 0.46082 0.46083 0.45592 0.4496 0.45284 0.44771 0.62724 0.62881 0.62822 0.62556 0.62487 0.62508 0.62641 0.62766 0.62722 0.62854 0.62845 0.62943 0.62806 0.62897 0.62648 0.62459 0.62188 0.62517 0.62596 0.62604 0.62552 0.62371 0.62152 0.6205 0.61384 0.61094 0.60799 0.60751 0.60431 0.59319 0.58887 0.58007 0.56773 0.56068 0.56347 0.55034 0.54715 0.5266 0.51823 0.50855 0.50568 0.49766 0.49706 0.48558 0.48217 0.47029 0.4605 0.45452 0.44617 0.43423 0.42634 0.41984 0.41569 0.41239 0.40949 0.40199 0.39435 0.39716 0.38153 0.38437 0.37445 0.3737 0.37561 0.36397 0.37015 0.36617 0.36683 0.35461 0.35857 0.3586 0.35538 0.35383 0.35191 0.34977 0.3576 0.35692 0.3522 0.3475 0.34123 0.34659 0.34027 0.57222 0.56711 0.5685 0.56933 0.56606 0.56951 0.5672 0.56753 0.5683 0.56948 0.56912 0.56902 0.57083 0.5692 0.56663 0.5699 0.5729 0.56541 0.57019 0.57014 0.5641 0.56404 0.56405 0.55905 0.55602 0.55502 0.54814 0.5409 0.54012 0.52681 0.52929 0.51834 0.51428 0.50281 0.50122 0.49447 0.48719 0.47851 0.47267 0.45802 0.4572 0.44763 0.43857 0.43626 0.43032 0.41858 0.40717 0.4026 0.39645 0.38411 0.37811 0.36997 0.37098 0.36768 0.37088 0.35246 0.35253 0.35819 0.33902 0.33888 0.33257 0.33551 0.34166 0.32708 0.32384 0.32743 0.32646 0.32002 0.32518 0.32655 0.32076 0.32229 0.31732 0.31281 0.32127 0.31906 0.31562 0.31599 0.31474 0.31678 0.31917
3 0.0041199 0.92261 0.91882 0.91795 0.91762 0.91756 0.91777 0.91826 0.91901 0.92017 0.92173 0.92355 0.92562 0.92798 0.93073 0.93371 0.93673 0.93959 0.94202 0.94414 0.94601 0.9468 0.94791 0.94723 0.94806 0.94635 0.9452 0.94095 0.9382 0.93572 0.9315 0.92768 0.91978 0.91469 0.90633 0.90236 0.8952 0.88652 0.88002 0.86654 0.85896 0.84852 0.84129 0.83394 0.82008 0.80772 0.79483 0.7838 0.77907 0.76534 0.75049 0.74274 0.73159 0.7233 0.71432 0.70457 0.69095 0.68195 0.66624 0.66125 0.64955 0.63969 0.62923 0.61346 0.60448 0.59116 0.57675 0.56911 0.55893 0.54898 0.53984 0.52631 0.51545 0.51245 0.50936 0.51363 0.5092 0.50613 0.50205 0.49363 0.49526 0.48417 0.92266 0.91888 0.918 0.91767 0.91761 0.91782 0.91831 0.91906 0.92022 0.92178 0.9236 0.92566 0.92803 0.93077 0.93375 0.93677 0.93963 0.94205 0.94417 0.94605 0.94683 0.94794 0.94727 0.9481 0.94638 0.94508 0.94098 0.9373 0.93471 0.93115 0.92694 0.92029 0.91342 0.90581 0.89917 0.89288 0.88587 0.87626 0.86486 0.85488 0.84536 0.83955 0.82654 0.81754 0.80347 0.78873 0.77988 0.77316 0.75821 0.74189 0.73576 0.72715 0.71691 0.70488 0.69663 0.68225 0.67269 0.65983 0.65283 0.64146 0.63191 0.61832 0.60684 0.59211 0.58631 0.56875 0.5622 0.54999 0.53812 0.53208 0.5223 0.51273 0.51333 0.50518 0.51193 0.50522 0.50425 0.50293 0.49234 0.49692 0.48809 0.78384 0.77939 0.77806 0.77752 0.77749 0.77775 0.77817 0.77864 0.77903 0.77948 0.77961 0.77889 0.77874 0.77929 0.77887 0.77927 0.77985 0.78095 0.7828 0.78443 0.78694 0.78956 0.78896 0.7925 0.79285 0.79414 0.78518 0.78388 0.77837 0.77176 0.76463 0.74833 0.73935 0.73378 0.72472 0.72274 0.71198 0.70154 0.69056 0.67971 0.67182 0.66716 0.66015 0.65002 0.63746 0.62789 0.61927 0.61427 0.60358 0.58733 0.58466 0.5731 0.56618 0.55626 0.5489 0.53716 0.53118 0.5227 0.51965 0.50917 0.50812 0.49697 0.4953 0.48872 0.48354 0.47828 0.47139 0.46204 0.46222 0.46455 0.45398 0.44333 0.44748 0.44587 0.44984 0.44155 0.44507 0.44423 0.43482 0.43599 0.43155 0.55033 0.54868 0.54675 0.54575 0.54542 0.54746 0.54852 0.54955 0.54995 0.5518 0.55428 0.55747 0.56037 0.56204 0.56364 0.56467 0.56727 0.56975 0.57257 0.576 0.57996 0.58258 0.58076 0.58083 0.58027 0.57768 0.57419 0.57398 0.56786 0.56485 0.55988 0.5511 0.54313 0.53616 0.53215 0.52365 0.51512 0.50488 0.49222 0.47817 0.48078 0.47773 0.47535 0.46468 0.45956 0.4455 0.43786 0.43568 0.4263 0.41561 0.41222 0.40637 0.39756 0.39619 0.39183 0.38662 0.37899 0.37535 0.37468 0.36815 0.36514 0.36775 0.36342 0.35753 0.35776 0.35401 0.35817 0.3472 0.34775 0.35009 0.34562 0.33907 0.34141 0.3378 0.34624 0.34382 0.34028 0.34171 0.32901 0.33817 0.3308 0.49296 0.4895 0.48954 0.49043 0.48862 0.4921 0.48884 0.48981 0.49435 0.49439 0.4977 0.49781 0.50217 0.5007 0.50391 0.50874 0.51445 0.51048 0.51863 0.51863 0.51607 0.518 0.51852 0.51972 0.5206 0.5188 0.51129 0.50852 0.50704 0.49929 0.50056 0.48961 0.48963 0.47816 0.47441 0.46844 0.4613 0.45231 0.44892 0.43079 0.43209 0.42647 0.41902 0.41551 0.4062 0.39629 0.3848 0.38264 0.37895 0.3631 0.36031 0.35591 0.35531 0.34914 0.34821 0.33918 0.33669 0.33767 0.33271 0.3239 0.32483 0.32623 0.32786 0.31862 0.31123 0.31527 0.31883 0.31419 0.31333 0.31824 0.30833 0.30632 0.30573 0.30124 0.31029 0.30375 0.3043 0.30956 0.30154 0.3058 0.30933
4 0.013905 0.85138 0.85048 0.84937 0.84947 0.85029 0.8514 0.85252 0.85362 0.85441 0.85472 0.85469 0.85436 0.85376 0.85293 0.85204 0.85132 0.85086 0.85067 0.85051 0.84998 0.84926 0.84868 0.84801 0.84733 0.84707 0.84587 0.8389 0.83722 0.83361 0.83192 0.83106 0.81953 0.81432 0.81095 0.80695 0.8018 0.79987 0.79106 0.78391 0.77968 0.77359 0.76729 0.76184 0.75082 0.74115 0.73294 0.72892 0.72051 0.7129 0.70162 0.69102 0.68618 0.67349 0.66333 0.65385 0.6495 0.63773 0.62508 0.61724 0.60561 0.59687 0.58725 0.57343 0.56424 0.55182 0.5405 0.52623 0.5236 0.50621 0.49617 0.48944 0.48141 0.47862 0.47165 0.46959 0.46734 0.46986 0.46652 0.45883 0.45226 0.45031 0.85144 0.85053 0.84942 0.84952 0.85035 0.85145 0.85257 0.85367 0.85446 0.85477 0.85474 0.8544 0.85381 0.85297 0.85208 0.85136 0.8509 0.85071 0.85055 0.85001 0.8493 0.84871 0.84805 0.84736 0.84707 0.84572 0.83825 0.83615 0.83252 0.83155 0.83007 0.81986 0.81134 0.81146 0.80237 0.80084 0.79638 0.78846 0.78095 0.77583 0.76882 0.76419 0.75552 0.74858 0.73619 0.72798 0.72138 0.71463 0.70773 0.69491 0.68362 0.68017 0.66744 0.6578 0.65081 0.64318 0.63106 0.61996 0.6088 0.59899 0.58691 0.57781 0.5628 0.55325 0.5421 0.53137 0.51639 0.51043 0.49818 0.48966 0.48411 0.47719 0.47588 0.46955 0.47012 0.46373 0.46156 0.46291 0.45832 0.45824 0.45024 0.68713 0.68668 0.68638 0.68772 0.68951 0.69101 0.69218 0.69326 0.69414 0.69449 0.69464 0.69487 0.69445 0.69335 0.69254 0.69161 0.69025 0.68881 0.68678 0.68535 0.68306 0.67953 0.67769 0.67458 0.67408 0.67066 0.65967 0.65652 0.65431 0.655 0.65329 0.63702 0.62844 0.6233 0.61494 0.61083 0.60595 0.59653 0.58599 0.58287 0.58014 0.57497 0.56865 0.56019 0.54756 0.54252 0.53858 0.53167 0.52771 0.51678 0.50808 0.5047 0.49542 0.48862 0.4818 0.48302 0.47442 0.46839 0.4599 0.45746 0.45139 0.44206 0.43801 0.43645 0.43252 0.42529 0.41542 0.41951 0.41279 0.40684 0.40786 0.40074 0.40391 0.39938 0.39572 0.39553 0.39456 0.39118 0.38432 0.38747 0.37912 0.47682 0.47571 0.47538 0.47586 0.47672 0.47851 0.47895 0.47982 0.48154 0.48247 0.4825 0.48193 0.48102 0.48013 0.47836 0.47732 0.47886 0.48072 0.48227 0.48393 0.48422 0.48327 0.48107 0.47965 0.47818 0.47758 0.47087 0.46813 0.46234 0.46088 0.46357 0.45056 0.44568 0.44062 0.42906 0.42899 0.42373 0.41675 0.40191 0.39932 0.40652 0.41054 0.40869 0.4017 0.38926 0.38478 0.38095 0.37655 0.37611 0.36787 0.3614 0.36064 0.35429 0.35179 0.34714 0.34518 0.34014 0.33393 0.3314 0.32879 0.32685 0.3254 0.3207 0.32335 0.31713 0.31888 0.31103 0.31564 0.30996 0.30786 0.31283 0.30327 0.30552 0.30312 0.3033 0.30346 0.30087 0.3051 0.29326 0.29831 0.29234 0.41702 0.41932 0.41953 0.4185 0.42029 0.42072 0.42316 0.42298 0.4276 0.42685 0.42514 0.4254 0.42482 0.42009 0.42355 0.42208 0.42153 0.41813 0.42277 0.41784 0.41789 0.41647 0.41669 0.41901 0.41937 0.4149 0.40621 0.40479 0.40172 0.39829 0.39904 0.38829 0.38771 0.38 0.37853 0.37116 0.37624 0.36356 0.36141 0.35412 0.3544 0.34961 0.35016 0.34451 0.33883 0.33614 0.32648 0.32418 0.32676 0.31442 0.31051 0.31027 0.30479 0.29961 0.30159 0.29905 0.29527 0.29618 0.28947 0.28069 0.28323 0.28211 0.28241 0.27885 0.27221 0.27725 0.27375 0.27802 0.27193 0.27784 0.26881 0.26954 0.26864 0.26788 0.26995 0.2634 0.26383 0.27003 0.26106 0.26413 0.26664
5 0.032959 0.73681 0.73547 0.73742 0.74144 0.74546 0.7495 0.75584 0.76243 0.76873 0.77382 0.77795 0.78264 0.7865 0.78923 0.79124 0.79284 0.79395 0.79435 0.79387 0.79328 0.79267 0.79176 0.7908 0.78995 0.78871 0.78647 0.77874 0.77424 0.77176 0.77079 0.76649 0.75902 0.75229 0.74858 0.7433 0.73815 0.73681 0.72687 0.71969 0.71468 0.70816 0.70503 0.69634 0.69189 0.67992 0.67301 0.66802 0.65945 0.65362 0.64065 0.63337 0.62647 0.61855 0.61201 0.60394 0.59636 0.58452 0.57588 0.56854 0.55688 0.54642 0.53448 0.52443 0.51403 0.50405 0.48972 0.47886 0.46278 0.45337 0.45033 0.44311 0.44162 0.43667 0.43391 0.42336 0.42759 0.4198 0.41704 0.41665 0.40411 0.41142 0.73364 0.73237 0.73452 0.73884 0.74317 0.74754 0.7542 0.7611 0.76762 0.77288 0.77719 0.78203 0.78602 0.78889 0.79103 0.79269 0.79385 0.79429 0.79384 0.79327 0.79268 0.79179 0.79082 0.78989 0.7885 0.78586 0.77777 0.77326 0.77151 0.7699 0.76665 0.7561 0.7521 0.74428 0.7413 0.73648 0.73253 0.72355 0.71523 0.70966 0.70576 0.69811 0.69197 0.68707 0.67266 0.66867 0.66166 0.65248 0.64741 0.63367 0.62589 0.61809 0.61174 0.60337 0.59207 0.58578 0.57703 0.5687 0.55837 0.54366 0.53508 0.52644 0.51567 0.50522 0.49398 0.48045 0.46729 0.45661 0.44686 0.44465 0.44113 0.43638 0.43532 0.43338 0.42738 0.42037 0.41907 0.41587 0.41759 0.4086 0.40746 0.52601 0.52555 0.5295 0.53622 0.5438 0.55131 0.56078 0.57115 0.58034 0.58714 0.59279 0.59836 0.60272 0.60575 0.60801 0.61001 0.61162 0.61268 0.61289 0.6125 0.61224 0.61163 0.6112 0.61103 0.60921 0.60609 0.5915 0.58517 0.58091 0.57637 0.57001 0.55431 0.54356 0.53315 0.52793 0.52049 0.51688 0.50689 0.49714 0.49402 0.49006 0.48571 0.48087 0.47585 0.46187 0.45758 0.4525 0.44799 0.44284 0.43171 0.42654 0.42244 0.41842 0.41446 0.40765 0.40408 0.39889 0.39357 0.39246 0.38721 0.37881 0.37983 0.3727 0.36792 0.36766 0.3614 0.3567 0.35549 0.35171 0.34846 0.3462 0.34797 0.34173 0.34066 0.33418 0.33877 0.3326 0.32799 0.32659 0.31731 0.31952 0.3659 0.3655 0.36762 0.37174 0.37591 0.37949 0.38606 0.39202 0.39699 0.40073 0.40326 0.40642 0.41005 0.41323 0.41646 0.4202 0.42227 0.42302 0.42236 0.42052 0.41816 0.41568 0.41429 0.41478 0.4166 0.41711 0.40916 0.40388 0.40169 0.39873 0.39223 0.38404 0.37755 0.3717 0.36633 0.361 0.35374 0.34743 0.34069 0.34535 0.34356 0.34636 0.34314 0.33465 0.32512 0.31801 0.31839 0.32257 0.31535 0.30695 0.30782 0.30242 0.3003 0.29606 0.29213 0.28918 0.28549 0.28641 0.28698 0.28163 0.27818 0.28013 0.27719 0.27714 0.27612 0.27161 0.26691 0.26674 0.26879 0.26648 0.26394 0.26751 0.26221 0.26344 0.25601 0.26027 0.25779 0.25732 0.25629 0.24865 0.25317 0.30381 0.30676 0.31037 0.31047 0.31865 0.32103 0.32796 0.33314 0.34019 0.34568 0.34851 0.35484 0.35745 0.36162 0.36313 0.35978 0.3626 0.36359 0.36344 0.36089 0.36351 0.36232 0.36349 0.36224 0.35936 0.35626 0.34647 0.34099 0.33982 0.34221 0.3349 0.33122 0.3279 0.31589 0.32062 0.30884 0.31426 0.30162 0.29471 0.29769 0.287 0.28826 0.29123 0.28384 0.27847 0.27542 0.26874 0.26815 0.26957 0.25502 0.2532 0.25741 0.2529 0.25215 0.24889 0.24627 0.24516 0.24201 0.23919 0.23639 0.2367 0.23818 0.23708 0.23447 0.23319 0.23204 0.22995 0.22709 0.22592 0.23572 0.2268 0.23255 0.22618 0.22796 0.22745 0.22192 0.22184 0.22569 0.22031 0.21309 0.22232
6 0.064373 0.65304 0.65266 0.65308 0.65432 0.65708 0.66013 0.6621 0.66489 0.66912 0.675 0.6813 0.68587 0.69045 0.6973 0.70479 0.71192 0.71792 0.72219 0.72724 0.73171 0.73569 0.73894 0.74126 0.74211 0.74264 0.74121 0.73273 0.73042 0.72843 0.72582 0.72295 0.7131 0.70617 0.69933 0.69637 0.69273 0.68598 0.68029 0.67292 0.66561 0.66359 0.65645 0.65186 0.64522 0.63315 0.62871 0.6216 0.6144 0.60903 0.59837 0.58976 0.58258 0.5759 0.56694 0.55741 0.55187 0.54458 0.53527 0.52356 0.51279 0.50435 0.49427 0.48434 0.47232 0.45947 0.44805 0.43595 0.42658 0.41881 0.40848 0.40527 0.39985 0.3991 0.39687 0.39557 0.38688 0.38315 0.38165 0.38012 0.3801 0.37636 0.61774 0.61775 0.61943 0.62232 ...Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(A,Z)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(a,z))'};
ff_graph_grid((tb_az_v{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(A,Z)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(a,z))'};
ff_graph_grid((tb_az_c{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
1.6 Analyze Marginal Value and MPC over Y(a,eta), Conditional On Kids, Marry, Age, Education
Income is generated by savings and shocks, what are the income levels generated by all the shock and savings points conditional on kids, marital status, age and educational levels. Plot on the Y axis MPC, and plot on the X axis income levels, use colors to first distinguish between different a levels, then use colors to distinguish between different eta levles.
Set Up date, Select Age 38, unmarried, no kids, lower education:
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
% 38 year old, unmarried, no kids, lower educated
% Only Household Head Shock Matters so select up to 'n_eta_H_grid'
mn_total_inc_jemk = total_inc_VFI(20,:,1:mp_params('n_eta_H_grid'),1,1,1);
mn_V_W_gain_check_use = ev20_jaeemk_check2 - ev20_jaeemk_check0;
mn_C_W_gain_check_use = ec20_jaeemk_check2 - ec20_jaeemk_check0;Select Age, Education, Marital, Kids Count:s
% Selections
it_age = 21; % +18
it_marital = 1; % 1 = unmarried
it_kids = 1; % 1 = kids is zero
it_educ = 1; % 1 = lower education
% Select: NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
mn_C_W_gain_check_jemk = mn_C_W_gain_check_use(it_age, :, 1:mp_params('n_eta_H_grid'), it_educ, it_marital, it_kids);
mn_V_W_gain_check_jemk = mn_V_W_gain_check_use(it_age, :, 1:mp_params('n_eta_H_grid'), it_educ, it_marital, it_kids);
% Reshape, so shock is the first dim, a is the second
mt_total_inc_jemk = permute(mn_total_inc_jemk,[3,2,1]);
mt_C_W_gain_check_jemk = permute(mn_C_W_gain_check_jemk,[3,2,1]);
mt_C_W_gain_check_jemk(mt_C_W_gain_check_jemk<=1e-10) = 1e-10;
mt_V_W_gain_check_jemk = permute(mn_V_W_gain_check_jemk,[3,2,1]);
mt_V_W_gain_check_jemk(mt_V_W_gain_check_jemk<=1e-10) = 1e-10;
% Generate meshed a and shock grid
[mt_eta_H, mt_a] = ndgrid(eta_H_grid(1:mp_params('n_eta_H_grid')), agrid);1.7 Marginal Value Gains, Color as Shock, Conditional on Age, Marital, Kids, and Education
How do shocks and a impact marginal value. First plot one asset level, variation comes only from increasingly higher shocks:
figure();
it_a = 1;
scatter((mt_total_inc_jemk(:,it_a)), (mt_V_W_gain_check_jemk(:,it_a)), 100);
title({'MN\_V\_W\_GAIN\_CHECK(Y(A, eta)), Lowest A, J38M0E0K0', ...
'Each Circle is A Different Shock Level'});
xlabel('Y(a,eta) = Spouse + Household head Income Joint');
ylabel('MN\_V\_W\_GAIN\_CHECK(A, eta)');
grid on;
grid minor;
figure();
it_shock = 1;
scatter(log(mt_total_inc_jemk(:,it_a)), log(mt_V_W_gain_check_jemk(:,it_a)), 100);
title({'MN\_V\_W\_GAIN\_CHECK(Y(A, eta)), Lowest A, J38M0E0K0', ...
'Each Circle is A Different Shock Level'});
xlabel(' of Y(a,eta) = Spouse + Household head Income Joint');
ylabel(' of MN\_V\_W\_GAIN\_CHECK(A, eta)');
grid on;
grid minor;
Plot all asset levels:
figure();
scatter((mt_total_inc_jemk(:)), (mt_V_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_V\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('income(a,eps)');
ylabel('MN\_V\_W\_GAIN\_CHECK(EM,J)');
grid on;
grid minor;
figure();
scatter((mt_total_inc_jemk(:)), log(mt_V_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_V\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('income(a,eps)');
ylabel('log of (MN\_V\_W\_GAIN\_CHECK(EM,J))');
xlim([0,7]);
grid on;
grid minor;
1.8 Marginal Consumption Gains, Color as Shock, Conditional on Age, Marital, Kids, and Education
How do shocks and a impact marginal value. First plot one asset level, variation comes only from increasingly higher shocks:
figure();
it_a = 50;
scatter(log(mt_total_inc_jemk(:,it_a)), mt_C_W_gain_check_jemk(:,it_a), 100);
title({'MN\_C\_W\_GAIN\_CHECK(Y(A, eta)), Given A Savings Level, J38M0E0K0', ...
'Each Circle is A Different shock Level'});
xlabel('Y(a,eta) = Spouse + Household head Income Joint');
ylabel('MN\_C\_W\_GAIN\_CHECK(A, eta)');
grid on;
grid minor;
Plot all asset levels:
figure();
scatter((mt_total_inc_jemk(:)), (mt_C_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_C\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('income(a,eps)');
ylabel('MN\_C\_W\_GAIN\_CHECK(EM,J)');
grid on;
grid minor;
figure();
scatter(log(mt_total_inc_jemk(:)), log(mt_C_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_C\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('log of income(a,eps)');
ylabel('log of (MN\_V\_W\_GAIN\_CHECK(EM,J))');
grid on;
grid minor;
1.9 Analyze Kids and Marriage and Age
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = [...
"k0M0", "K1M0", "K2M0", "K3M0", "K4M0", ...
"k0M1", "K1M1", "K2M1", "K3M1", "K4M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = {...
'o', 'd' ,'s', 'x', '*', ...
'o', 'd', 's', 'x', '*'};
mp_support_graph('cl_colors') = {...
'red', 'red', 'red', 'red', 'red'...
'blue', 'blue', 'blue', 'blue', 'blue'};MEAN(VAL(KM,J)), MEAN(AP(KM,J)), MEAN(C(KM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,4,1,6,5];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(KM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(KM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 0.038521 0.037547 0.036373 0.033138 0.030444 0.028185 0.026271 0.024644 0.023252 0.022051 0.021021 0.020126 0.019349 0.018555 0.017974 0.017461 0.017021 0.016639 0.016303 0.016017 0.015767 0.015556 0.015378 0.015182 0.015059 0.01496 0.014883 0.014821 0.014781 0.014758 0.014754 0.01476 0.014775 0.014883 0.014928 0.014979 0.01504 0.01511 0.015189 0.015274 0.015367 0.015464 0.015564 0.016273 0.016397 0.01656 0.016907 0.011788 0.011679 0.011567 0.011453 0.011336 0.011216 0.011093 0.010968 0.01084 0.010707 0.010568 0.010425 0.010278 0.010126 0.0099699 0.0098085 0.0096426 0.0094728 0.0093001 0.0091258 0.0089508 0.0087765 0.008604 0.0084348 0.0082704 0.0081113 0.0079579 0.00781 0.0076677 0.0075304 0.0073967 0.0072599 0.0071067 0.0069026 0.0065638 0.0058098
2 2 0 0.05341 0.052108 0.050487 0.045932 0.042132 0.038937 0.036221 0.033907 0.03192 0.030198 0.028715 0.027421 0.02629 0.025133 0.024277 0.023515 0.022856 0.022278 0.021766 0.021323 0.02093 0.020593 0.020302 0.019985 0.019772 0.019593 0.019445 0.019319 0.019224 0.019153 0.01911 0.019082 0.019067 0.019188 0.019219 0.019262 0.01932 0.019394 0.019484 0.019589 0.019711 0.019846 0.019995 0.021032 0.021228 0.021465 0.021872 0.014147 0.014025 0.013901 0.013776 0.01365 0.013521 0.01339 0.013257 0.013123 0.012986 0.012843 0.012697 0.012548 0.012395 0.012239 0.012078 0.011913 0.011745 0.011575 0.011404 0.011234 0.011066 0.010902 0.010742 0.010587 0.010436 0.010291 0.01015 0.010013 0.0098794 0.0097488 0.0096152 0.0094665 0.0092732 0.0089601 0.0082163
3 3 0 0.063385 0.062078 0.06036 0.05494 0.050421 0.046624 0.043398 0.04065 0.038291 0.036247 0.034487 0.03295 0.031607 0.030224 0.029206 0.028299 0.027513 0.026823 0.02621 0.025678 0.025205 0.024799 0.024447 0.02406 0.023799 0.023579 0.023395 0.023236 0.023116 0.023024 0.022966 0.022925 0.022901 0.023044 0.023078 0.023126 0.023195 0.023286 0.023397 0.023531 0.023686 0.02386 0.024051 0.025324 0.025548 0.025777 0.026109 0.016705 0.016569 0.016432 0.016295 0.016156 0.016015 0.015873 0.01573 0.015586 0.015438 0.015286 0.01513 0.014971 0.014809 0.014642 0.01447 0.014294 0.014115 0.013934 0.013754 0.013574 0.013398 0.013224 0.013053 0.012886 0.012723 0.012563 0.012406 0.012252 0.012101 0.011952 0.011802 0.011641 0.011435 0.01108 0.010063
4 4 0 0.072378 0.070987 0.069103 0.062915 0.057756 0.053423 0.049741 0.046605 0.043915 0.041582 0.039574 0.03782 0.036287 0.034706 0.033543 0.032507 0.031609 0.03082 0.030119 0.02951 0.028968 0.028503 0.028099 0.027653 0.027354 0.0271 0.026888 0.026705 0.026566 0.02646 0.026393 0.026346 0.026319 0.026485 0.026526 0.026584 0.026668 0.026777 0.026912 0.027072 0.027258 0.027463 0.027681 0.029138 0.029366 0.029574 0.029908 0.019162 0.019015 0.018868 0.01872 0.01857 0.018419 0.018266 0.018113 0.017959 0.017801 0.017638 0.017471 0.0173 0.017124 0.016943 0.016757 0.016566 0.016372 0.016174 0.015976 0.015779 0.015584 0.01539 0.0152 0.015013 0.014828 0.014645 0.014465 0.014287 0.014112 0.01394 0.013767 0.013582 0.013337 0.012873 0.01162
5 5 0 0.079908 0.078513 0.076557 0.069742 0.064064 0.059298 0.055251 0.051805 0.048851 0.046292 0.04409 0.042168 0.040489 0.038753 0.037482 0.036349 0.035369 0.034507 0.033743 0.03308 0.032489 0.031984 0.031544 0.031058 0.030734 0.03046 0.030231 0.030035 0.029887 0.029776 0.029707 0.029662 0.029639 0.029831 0.029884 0.029956 0.030057 0.030186 0.030343 0.030526 0.030735 0.03096 0.031191 0.032791 0.033011 0.033212 0.033577 0.021733 0.021571 0.021409 0.021247 0.021082 0.020916 0.020748 0.020579 0.020409 0.020234 0.020053 0.019866 0.019674 0.019476 0.019272 0.019061 0.018844 0.018622 0.018395 0.018167 0.017939 0.017711 0.017485 0.017261 0.01704 0.016822 0.016607 0.016394 0.016185 0.015978 0.015777 0.015573 0.015349 0.015036 0.01446 0.012991
6 1 1 0.012602 0.012065 0.011549 0.010425 0.0094851 0.0086941 0.008021 0.0074489 0.0069596 0.0065366 0.0061771 0.0058652 0.0055961 0.0053205 0.0051246 0.0049518 0.0048077 0.0046847 0.0045788 0.0044924 0.004419 0.0043626 0.0043192 0.0042702 0.0042498 0.0042398 0.0042396 0.0042463 0.004264 0.0042904 0.0043274 0.0043702 0.0044186 0.0045076 0.0045776 0.0046533 0.0047373 0.0048307 0.0049344 0.0050497 0.0051787 0.0053229 0.0054861 0.0059321 0.0061688 0.0064673 0.0069298 0.0061231 0.0058519 0.0059011 0.0059493 0.0059957 0.0060414 0.0060855 0.0061224 0.0061549 0.006178 0.0061911 0.0062016 0.0062129 0.0062208 0.0062169 0.006195 0.0061673 0.0061386 0.0061049 0.0060596 0.0060148 0.0059735 0.005923 0.0058642 0.0058051 0.005744 0.0056878 0.0056296 0.0055657 0.0054929 0.0054172 0.0053386 0.0052422 0.0050904 0.0048434 0.0042971
7 2 1 0.016779 0.016071 0.015392 0.013893 0.012636 0.011578 0.010681 0.0099168 0.0092612 0.0086935 0.0082088 0.007787 0.0074214 0.0070468 0.0067771 0.0065385 0.0063379 0.0061659 0.0060166 0.0058931 0.0057867 0.0057027 0.0056356 0.0055611 0.005524 0.0055006 0.0054897 0.0054877 0.0054998 0.005523 0.0055598 0.0056038 0.0056549 0.0057586 0.0058367 0.0059217 0.0060175 0.0061246 0.0062433 0.0063752 0.006523 0.0066878 0.0068734 0.0074216 0.007683 0.00801 0.0084946 0.0069974 0.0066651 0.0067158 0.0067716 0.0068223 0.0068762 0.0069178 0.0069584 0.0069908 0.0070194 0.0070416 0.0070616 0.0070794 0.0070855 0.0070764 0.0070604 0.0070494 0.0070306 0.0070037 0.0069716 0.0069341 0.006894 0.0068482 0.0067984 0.006743 0.0066964 0.0066461 0.0065822 0.0065159 0.0064469 0.0063809 0.0063099 0.006213 0.0060697 0.0058279 0.0052563
8 3 1 0.02027 0.019455 0.018664 0.016853 0.015336 0.014061 0.012973 0.012047 0.011256 0.010572 0.0099893 0.0094812 0.0090403 0.0085875 0.0082617 0.007973 0.0077303 0.0075217 0.0073401 0.0071892 0.0070586 0.0069548 0.0068713 0.0067784 0.0067309 0.0066998 0.0066836 0.0066781 0.0066893 0.0067135 0.0067537 0.0068023 0.0068586 0.0069786 0.0070668 0.0071632 0.0072721 0.0073943 0.00753 0.0076804 0.0078477 0.0080326 0.0082401 0.0088849 0.0091687 0.0095081 0.0099965 0.0077973 0.0074124 0.007474 0.0075299 0.0075948 0.0076528 0.0077085 0.0077515 0.0077948 0.0078281 0.0078591 0.0078824 0.0079084 0.0079232 0.0079234 0.0079119 0.0079015 0.0078907 0.0078699 0.0078439 0.0078097 0.0077745 0.0077336 0.0076868 0.0076343 0.0075811 0.0075342 0.007477 0.0074075 0.0073362 0.0072636 0.0071921 0.0070989 0.006947 0.0066846 0.0060133
9 4 1 0.024225 0.023287 0.02236 0.020204 0.018398 0.016876 0.015579 0.014477 0.013532 0.012712 0.01201 0.011399 0.010871 0.010329 0.0099384 0.0095919 0.0092991 0.0090465 0.0088262 0.0086424 0.008483 0.0083551 0.0082514 0.0081362 0.0080751 0.0080332 0.0080091 0.0079974 0.0080056 0.0080291 0.0080714 0.0081234 0.0081847 0.0083213 0.0084192 0.0085262 0.0086476 0.0087843 0.0089367 0.0091064 0.0092951 0.009502 0.0097292 0.010464 0.010762 0.011108 0.01159 0.008575 0.0081463 0.0082155 0.0082791 0.0083513 0.0084183 0.008484 0.0085353 0.0085859 0.0086259 0.0086646 0.0086953 0.0087274 0.0087498 0.0087581 0.0087516 0.0087434 0.0087376 0.0087221 0.0086993 0.0086697 0.0086367 0.008597 0.0085504 0.0084983 0.0084405 0.0083907 0.0083342 0.0082629 0.0081866 0.0081082 0.0080293 0.0079326 0.007765 0.0074586 0.0067031
10 5 1 0.029524 0.028486 0.027439 0.024819 0.02263 0.020789 0.019224 0.017888 0.016746 0.015759 0.014913 0.014174 0.013532 0.012872 0.012397 0.011977 0.011622 0.011315 0.011046 0.010821 0.010625 0.010468 0.010339 0.010196 0.010119 0.010065 0.010032 0.010014 0.01002 0.010044 0.010091 0.010148 0.010217 0.010377 0.010488 0.01061 0.010749 0.010906 0.01108 0.011273 0.011484 0.011711 0.011956 0.012816 0.013127 0.013474 0.013936 0.0097772 0.0093091 0.0093898 0.0094602 0.0095346 0.0095991 0.0096648 0.0097236 0.0097883 0.0098435 0.009886 0.0099137 0.0099367 0.0099654 0.0099838 0.0099906 0.0099905 0.0099827 0.0099651 0.0099346 0.0098982 0.0098535 0.0098182 0.0097756 0.0097181 0.009646 0.0095713 0.009503 0.00943 0.0093475 0.009261 0.0091647 0.0090452 0.0088616 0.0085017 0.007613
% Consumption Function
st_title = ['MEAN(MN_MPC_U_GAIN_CHECK(KM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_c = ff_summ_nd_array(st_title, mn_MPC_U_gain_share_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_MPC_U_GAIN_CHECK(KM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 0.08473 0.090523 0.10389 0.10174 0.099813 0.09806 0.096241 0.094688 0.093677 0.092234 0.092289 0.09047 0.089361 0.087454 0.087274 0.086491 0.085361 0.085375 0.084384 0.084178 0.083468 0.083393 0.083115 0.081871 0.081453 0.082167 0.080836 0.080698 0.080679 0.080755 0.08039 0.081075 0.080402 0.080161 0.080721 0.080021 0.081509 0.080844 0.08114 0.081549 0.082208 0.083507 0.083585 0.087243 0.089682 0.091383 0.09765 0.11476 0.11753 0.12009 0.12294 0.12517 0.1275 0.1303 0.13307 0.13597 0.13912 0.14252 0.14616 0.15006 0.15435 0.15916 0.16435 0.17023 0.1766 0.18058 0.18768 0.19434 0.20151 0.20962 0.21948 0.22764 0.23974 0.25319 0.26636 0.28245 0.30293 0.32328 0.34988 0.39178 0.4579 0.5921 0.99997
2 2 0 0.092218 0.098831 0.11367 0.11201 0.11015 0.10846 0.10752 0.10587 0.10513 0.10406 0.10342 0.10245 0.10181 0.10089 0.1011 0.10039 0.10061 0.10034 0.099868 0.099677 0.099481 0.099125 0.10002 0.099379 0.098612 0.099254 0.098899 0.099174 0.098645 0.098524 0.098249 0.098639 0.098559 0.098516 0.099186 0.098297 0.098675 0.098554 0.098408 0.098517 0.09974 0.10084 0.10084 0.10334 0.10442 0.1067 0.11281 0.14765 0.14986 0.15218 0.15457 0.15704 0.15963 0.16239 0.16534 0.1686 0.17212 0.17592 0.17997 0.18427 0.18886 0.19263 0.1966 0.20145 0.20709 0.21353 0.22097 0.22941 0.23656 0.24548 0.25609 0.26727 0.27787 0.28989 0.30532 0.3176 0.33826 0.35795 0.38423 0.42904 0.49654 0.62347 0.99997
3 3 0 0.1021 0.11001 0.12659 0.12367 0.12143 0.11972 0.11751 0.11661 0.11453 0.11379 0.11271 0.11174 0.11099 0.10904 0.10827 0.10838 0.10814 0.10684 0.10761 0.10716 0.10735 0.10687 0.10651 0.10673 0.10625 0.10655 0.1068 0.10658 0.10679 0.10632 0.1062 0.10623 0.10563 0.10634 0.10721 0.10619 0.10654 0.1057 0.1064 0.10537 0.10587 0.10693 0.10716 0.11005 0.11187 0.11366 0.11889 0.15321 0.15551 0.15796 0.1605 0.16316 0.16603 0.16906 0.17223 0.17549 0.17901 0.18277 0.18672 0.19056 0.19276 0.19748 0.20241 0.20827 0.21505 0.22244 0.23038 0.23705 0.24556 0.25496 0.26517 0.27639 0.2853 0.29836 0.31299 0.32492 0.34377 0.36239 0.38943 0.43049 0.49528 0.61613 0.99997
4 4 0 0.10668 0.11447 0.13204 0.12929 0.12652 0.12473 0.12223 0.12058 0.11889 0.1172 0.11693 0.11506 0.11467 0.11304 0.11206 0.11189 0.11137 0.11128 0.11132 0.11024 0.11087 0.11047 0.11141 0.11004 0.10985 0.10986 0.11004 0.11037 0.11017 0.11 0.11092 0.1103 0.11039 0.1109 0.11055 0.11012 0.11011 0.10983 0.10861 0.1084 0.10888 0.10833 0.11066 0.11214 0.11299 0.11664 0.12234 0.1551 0.15738 0.15968 0.1621 0.16471 0.16752 0.1704 0.17343 0.17663 0.18005 0.18372 0.1876 0.18956 0.19365 0.19818 0.20349 0.20943 0.21605 0.22325 0.2312 0.23665 0.24627 0.2559 0.26627 0.27599 0.28594 0.29857 0.31262 0.3243 0.34228 0.35881 0.38336 0.42164 0.483 0.60347 0.99997
5 5 0 0.11273 0.11986 0.13778 0.13469 0.13217 0.12887 0.12659 0.12425 0.1225 0.12015 0.11927 0.1181 0.11661 0.11464 0.1141 0.11321 0.11319 0.11192 0.11141 0.11117 0.11085 0.11185 0.11047 0.11134 0.11032 0.10981 0.11054 0.10996 0.11007 0.10996 0.11058 0.11039 0.11038 0.11075 0.11075 0.11067 0.11042 0.11036 0.10969 0.10907 0.10851 0.10916 0.1101 0.11209 0.11364 0.11671 0.12294 0.1535 0.15585 0.1582 0.16061 0.16316 0.1659 0.16865 0.17159 0.17469 0.17793 0.18139 0.18461 0.1866 0.19074 0.19501 0.2 0.20584 0.21236 0.21954 0.22748 0.23262 0.2418 0.2513 0.26135 0.2726 0.2813 0.29339 0.30751 0.31879 0.33631 0.35206 0.37514 0.41087 0.47179 0.59854 0.99997
6 1 1 0.11128 0.1152 0.12146 0.1198 0.11906 0.11801 0.11699 0.11642 0.1158 0.11502 0.11462 0.11361 0.11263 0.11155 0.11125 0.11052 0.1098 0.10912 0.10862 0.10785 0.10708 0.10659 0.10556 0.10547 0.10449 0.10414 0.10305 0.10265 0.10184 0.10117 0.10065 0.099957 0.099329 0.099118 0.097918 0.096483 0.095927 0.095003 0.094662 0.094437 0.094419 0.095044 0.094475 0.095787 0.096313 0.096703 0.098525 0.13019 0.13201 0.12687 0.13567 0.13494 0.13608 0.13945 0.14089 0.1356 0.13914 0.13956 0.1487 0.15049 0.16247 0.15749 0.15634 0.16863 0.17924 0.17755 0.18291 0.19362 0.20345 0.20622 0.21604 0.22489 0.23626 0.25779 0.26207 0.27228 0.28647 0.30849 0.34999 0.37967 0.44701 0.59372 0.99998
7 2 1 0.11215 0.11651 0.1231 0.12179 0.12075 0.11977 0.1181 0.11776 0.1171 0.11645 0.11708 0.11686 0.11706 0.11605 0.11531 0.11491 0.11464 0.11431 0.11451 0.1137 0.11374 0.11291 0.11284 0.11203 0.11162 0.11112 0.11042 0.11007 0.1096 0.10924 0.10887 0.10807 0.10789 0.10777 0.10756 0.10649 0.10545 0.10451 0.1042 0.10315 0.10353 0.1028 0.10313 0.10427 0.10503 0.10552 0.10855 0.14063 0.13705 0.13657 0.14239 0.14259 0.14501 0.14392 0.14966 0.14834 0.15486 0.15467 0.16649 0.16632 0.16411 0.16828 0.18031 0.18764 0.18836 0.1957 0.19871 0.21141 0.21868 0.22538 0.23105 0.24429 0.26678 0.26343 0.27173 0.29021 0.30696 0.34244 0.36421 0.39653 0.47517 0.60581 0.99998
8 3 1 0.11764 0.12253 0.13117 0.1281 0.12731 0.12672 0.1253 0.12442 0.12279 0.12179 0.12126 0.12075 0.12024 0.11928 0.11916 0.11849 0.11811 0.11789 0.11809 0.11796 0.11788 0.11757 0.11726 0.11651 0.11581 0.11573 0.11529 0.11461 0.11469 0.11345 0.11322 0.11252 0.11182 0.11108 0.11081 0.11018 0.10996 0.10936 0.10859 0.10809 0.10808 0.10766 0.10767 0.10919 0.10951 0.11054 0.1145 0.14498 0.13977 0.14227 0.14586 0.14886 0.15279 0.14969 0.15017 0.15518 0.16098 0.16314 0.16377 0.17128 0.17755 0.17264 0.17644 0.19387 0.20084 0.20168 0.2084 0.21562 0.22632 0.23195 0.23978 0.24528 0.26434 0.27926 0.2807 0.29514 0.31132 0.34072 0.37702 0.4013 0.46706 0.60175 0.99999
9 4 1 0.11935 0.12502 0.13186 0.13059 0.1313 0.12843 0.12706 0.12691 0.12574 0.12437 0.12343 0.12301 0.12213 0.12066 0.12016 0.11933 0.1193 0.11954 0.11921 0.11904 0.11844 0.11838 0.11808 0.11793 0.11741 0.11743 0.11695 0.11702 0.1159 0.1158 0.11527 0.11468 0.11436 0.11435 0.11382 0.11328 0.113 0.11203 0.11186 0.11139 0.11129 0.11045 0.11081 0.11147 0.11261 0.11414 0.11873 0.14763 0.1431 0.14562 0.14564 0.15314 0.15223 0.15236 0.15328 0.1571 0.16333 0.16619 0.16678 0.1753 0.18057 0.17589 0.17915 0.196 0.20402 0.20428 0.21108 0.21538 0.22827 0.23484 0.24219 0.25109 0.2647 0.28291 0.28691 0.29336 0.3121 0.33461 0.37483 0.40129 0.45526 0.59747 0.99998
10 5 1 0.12647 0.1319 0.14032 0.13889 0.13523 0.13356 0.13395 0.13088 0.12888 0.12759 0.12732 0.12712 0.12545 0.12331 0.12254 0.12235 0.12195 0.12181 0.12149 0.12105 0.12059 0.11994 0.11963 0.11906 0.11875 0.11858 0.11819 0.1176 0.11727 0.11715 0.11683 0.11573 0.11554 0.11532 0.11497 0.1139 0.11401 0.11333 0.11304 0.11277 0.11185 0.11105 0.11175 0.11358 0.11452 0.1164 0.12172 0.15179 0.1537 0.1471 0.14655 0.15145 0.1561 0.15771 0.15722 0.16391 0.16737 0.1645 0.16496 0.17939 0.18589 0.18267 0.18918 0.19331 0.19916 0.20456 0.21258 0.21987 0.22632 0.24384 0.2452 0.2487 0.25824 0.27058 0.2954 0.29931 0.31252 0.33094 0.35873 0.40647 0.45714 0.58791 0.99998 Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(KM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(KM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(KM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(KM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
1.10 Analyze Education and Marriage
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = ["E0M0", "E1M0", "E0M1", "E1M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = {'*', 'p', '*','p' };
mp_support_graph('cl_colors') = {'red', 'red', 'blue', 'blue'};MEAN(VAL(EM,J)), MEAN(AP(EM,J)), MEAN(C(EM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,6,1,4,5];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(EM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(EM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 0.062739 0.061743 0.060475 0.056968 0.053854 0.051093 0.048616 0.046412 0.044437 0.042651 0.041055 0.039619 0.038313 0.036895 0.035849 0.034892 0.034041 0.033271 0.032579 0.031956 0.031384 0.030885 0.030439 0.029942 0.029595 0.029292 0.02903 0.028807 0.028621 0.02847 0.028354 0.02827 0.028203 0.028347 0.028367 0.028403 0.028468 0.028561 0.028683 0.028831 0.029003 0.029195 0.029398 0.030892 0.031088 0.031269 0.031521 0.018998 0.018846 0.018692 0.018537 0.018379 0.018219 0.018057 0.017894 0.017729 0.017559 0.017384 0.017203 0.017018 0.016828 0.016633 0.016431 0.016224 0.016012 0.015797 0.015581 0.015365 0.015151 0.01494 0.014733 0.014529 0.014331 0.014137 0.013947 0.013761 0.013579 0.013401 0.013221 0.013023 0.012759 0.012293 0.011103
2 1 0 0.060302 0.05875 0.056677 0.049699 0.044073 0.039493 0.035736 0.032632 0.030054 0.027897 0.0261 0.024575 0.023297 0.022053 0.021144 0.02036 0.019707 0.019156 0.018677 0.018287 0.017959 0.01769 0.017469 0.017234 0.017093 0.016985 0.016907 0.01684 0.016809 0.016798 0.016818 0.016841 0.016877 0.017025 0.017087 0.01716 0.017245 0.01734 0.017447 0.017566 0.017699 0.017842 0.017995 0.018931 0.019132 0.019366 0.019828 0.014416 0.014298 0.014179 0.01406 0.013938 0.013815 0.01369 0.013565 0.013437 0.013307 0.013172 0.013032 0.01289 0.012744 0.012594 0.012439 0.01228 0.012119 0.011955 0.01179 0.011625 0.011463 0.011302 0.011144 0.010989 0.010838 0.010689 0.010543 0.010401 0.010261 0.010124 0.009986 0.0098352 0.0096347 0.0092813 0.0083767
3 0 1 0.021794 0.020986 0.0202 0.018729 0.01744 0.016317 0.015324 0.014455 0.013687 0.013001 0.012399 0.011866 0.011388 0.010883 0.01052 0.010192 0.0099117 0.0096644 0.0094501 0.0092645 0.0090987 0.0089654 0.0088552 0.0087299 0.0086617 0.0086126 0.0085816 0.0085678 0.0085702 0.0085892 0.0086238 0.0086738 0.0087311 0.0088721 0.0089757 0.0090875 0.0092158 0.0093618 0.0095258 0.0097094 0.0099135 0.01014 0.01039 0.011185 0.011514 0.011911 0.012455 0.0090346 0.0086058 0.0086734 0.0087392 0.0088091 0.0088738 0.0089327 0.0089847 0.0090431 0.0090838 0.0091075 0.0091276 0.0091601 0.0091802 0.0091786 0.0091669 0.0091553 0.0091354 0.009103 0.0090682 0.0090284 0.0089863 0.008935 0.008874 0.0088062 0.0087403 0.0086798 0.0086114 0.0085321 0.0084443 0.0083535 0.0082608 0.0081457 0.0079649 0.0076423 0.0068479
4 1 1 0.019566 0.01876 0.017962 0.015748 0.013954 0.012482 0.011267 0.010256 0.0094143 0.0087078 0.0081205 0.007617 0.007196 0.006779 0.00648 0.0062205 0.0060069 0.005829 0.0056729 0.0055508 0.0054504 0.0053718 0.0053115 0.0052469 0.0052177 0.0052026 0.0052 0.0052016 0.0052212 0.0052509 0.0052973 0.0053455 0.0054024 0.0055051 0.0055797 0.0056622 0.0057535 0.005854 0.0059641 0.0060845 0.0062178 0.0063628 0.0065239 0.0070228 0.0072498 0.0075164 0.007923 0.0066734 0.0063481 0.0064051 0.0064568 0.0065104 0.0065613 0.0066115 0.0066518 0.0066828 0.0067142 0.0067495 0.0067743 0.0067858 0.0067976 0.0068048 0.0067969 0.0067855 0.0067767 0.0067633 0.0067354 0.0067022 0.0066665 0.006633 0.0065962 0.0065533 0.0065029 0.0064523 0.006399 0.0063407 0.0062798 0.0062188 0.0061531 0.006067 0.0059286 0.0056841 0.0051052
% Consumption
st_title = ['MEAN(MN_MPC_U_GAIN_CHECK(EM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_c = ff_summ_nd_array(st_title, mn_MPC_U_gain_share_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_MPC_U_GAIN_CHECK(EM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 0.091561 0.095658 0.10516 0.10456 0.10443 0.10416 0.10379 0.10374 0.10347 0.10316 0.10361 0.10319 0.10307 0.10229 0.10229 0.10231 0.10248 0.10182 0.10231 0.10233 0.10209 0.10221 0.10253 0.10216 0.10144 0.10219 0.10186 0.10173 0.10179 0.1017 0.10116 0.10145 0.10165 0.10135 0.10202 0.10175 0.10222 0.1016 0.10151 0.1012 0.10133 0.10223 0.1034 0.10581 0.10743 0.11011 0.11663 0.1434 0.14564 0.14801 0.15063 0.15327 0.15602 0.15884 0.16177 0.16488 0.16824 0.17185 0.1756 0.17889 0.18281 0.18715 0.1919 0.19735 0.20364 0.21002 0.21778 0.22421 0.23251 0.24156 0.25175 0.26205 0.27233 0.28504 0.29936 0.31237 0.3319 0.3501 0.37536 0.4155 0.4796 0.60603 0.99998
2 1 0 0.10782 0.11782 0.14043 0.136 0.1316 0.12778 0.12425 0.12106 0.11842 0.11582 0.11424 0.11193 0.1103 0.10774 0.10683 0.10584 0.10498 0.10448 0.10352 0.10264 0.10272 0.10247 0.10208 0.10159 0.10116 0.10087 0.10099 0.10098 0.10075 0.10052 0.10138 0.1012 0.10049 0.10132 0.10135 0.10037 0.10068 0.10051 0.10019 0.099966 0.10076 0.10127 0.10153 0.10413 0.10561 0.10793 0.11322 0.14628 0.14881 0.15124 0.15366 0.15603 0.15861 0.16149 0.1645 0.16767 0.17105 0.17468 0.17842 0.18152 0.18533 0.18983 0.19484 0.20073 0.20722 0.21371 0.2213 0.22782 0.23617 0.24535 0.2556 0.26591 0.27573 0.28833 0.30256 0.31485 0.33352 0.3517 0.37745 0.41803 0.4822 0.60745 0.99997
3 0 1 0.10916 0.11292 0.11733 0.11721 0.11716 0.11661 0.11651 0.11692 0.11712 0.11669 0.11687 0.11705 0.11701 0.11611 0.11584 0.1156 0.11547 0.11552 0.11547 0.1151 0.11461 0.11424 0.11382 0.11347 0.11301 0.11267 0.11216 0.11175 0.11142 0.11076 0.11035 0.10966 0.10898 0.10871 0.10855 0.10767 0.10748 0.10679 0.10654 0.10636 0.10611 0.10578 0.10597 0.10707 0.10811 0.10888 0.11295 0.14263 0.13758 0.13675 0.1407 0.14199 0.14524 0.14629 0.15115 0.15425 0.15031 0.15131 0.16231 0.17179 0.16902 0.16899 0.17603 0.18869 0.18718 0.19462 0.20253 0.21293 0.21822 0.22416 0.23034 0.23995 0.26136 0.27005 0.27923 0.28835 0.30269 0.32894 0.36307 0.39676 0.46172 0.5959 0.99998
4 1 1 0.1256 0.13155 0.14183 0.13846 0.1363 0.13399 0.13205 0.12964 0.12701 0.12539 0.12461 0.12349 0.12199 0.12023 0.11953 0.11864 0.11805 0.11755 0.1173 0.11674 0.11648 0.11591 0.11553 0.11494 0.11423 0.11414 0.1134 0.11303 0.1123 0.11196 0.11158 0.11073 0.11059 0.11034 0.10948 0.10846 0.10786 0.1069 0.1064 0.10557 0.10555 0.10502 0.10516 0.10665 0.10709 0.10844 0.11186 0.14346 0.14467 0.14262 0.14574 0.1504 0.15163 0.15096 0.14934 0.1498 0.16396 0.16392 0.16197 0.16532 0.17921 0.1738 0.17654 0.18709 0.20147 0.19889 0.20294 0.20943 0.22299 0.23273 0.23937 0.24575 0.25477 0.27154 0.2795 0.29177 0.30905 0.33394 0.36684 0.39735 0.45894 0.59877 0.99999 Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(EM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(EM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(EM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(EM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
